Methods and devices for detecting diabetic nephropathy and associated disorders

ABSTRACT

Methods and devices for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder in a mammal are described. In particular, methods and devices for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal are described.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 14/643,873, filed Mar. 10, 2015, which is a continuation of Ser. No. 12/852,282, filed Aug. 6, 2010, pending, which claims the priority of U.S. provisional application Ser. No. 61/327,389, filed Apr. 23, 2010, and U.S. provisional application Ser. No. 61/232,091, filed Aug. 7, 2009, each of which is hereby incorporated by reference in its entirety and is related to U.S. patent application Ser. Nos. 12/852,152; 12/852,202; 12/852,236; 12/852,295; 12/852,312; and Ser. No. 12/852,322, the entire contents of which are incorporated herein by reference.

FIELD OF THE INVENTION

The invention encompasses methods and devices for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder in a mammal. In particular, the present invention provides methods and devices for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal.

BACKGROUND OF THE INVENTION

The urinary system, in particular the kidneys, perform several critical functions such as maintaining electrolyte balance and eliminating toxins from the bloodstream. In the human body, the pair of kidneys together process roughly 20% of the total cardiac output, amounting to about 1 L/min in a 70-kg adult male. Because compounds in circulation are concentrated in the kidney up to 1000-fold relative to the plasma concentration, the kidney is especially vulnerable to injury due to exposure to toxic compounds.

Diabetic nephropathy is the most common cause of chronic kidney failure and end-stage kidney disease in the United States. People with both type 1 and type 2 diabetes are at risk. Existing diagnostic tests such as BUN and serum creatine tests typically detect only advanced stages of kidney damage. Other diagnostic tests such as kidney tissue biopsies or CAT scans have the advantage of enhanced sensitivity to earlier stages of kidney damage, but these tests are also generally costly, slow, and/or invasive.

A need exists in the art for a fast, simple, reliable, and sensitive method of detecting diabetic nephropathy or an associated disorder. In a clinical setting, the early detection of kidney damage would help medical practitioners to diagnose and treat kidney damage more quickly and effectively.

SUMMARY OF THE INVENTION

The present invention provides methods and devices for diagnosing, monitoring, or determining a renal disorder in a mammal. In particular, the present invention provides methods and devices for diagnosing, monitoring, or determining a renal disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal.

One aspect of the invention encompasses a method for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder in a mammal. The method typically comprises providing a test sample comprising a sample of bodily fluid taken from the mammal. Then, the method comprises determining a combination of sample concentrations for three or more sample analytes in the test sample, wherein the sample analytes are selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. The combination of sample concentrations may be compared to a data set comprising at least one entry, wherein each entry of the data set comprises a list comprising three or more minimum diagnostic concentrations indicative of diabetic nephropathy or an associated disorder. Each minimum diagnostic concentration comprises a maximum of a range of analyte concentrations for a healthy mammal. Next, the method comprises determining a matching entry of the dataset in which all minimum diagnostic concentrations are less than the corresponding sample concentrations and identifying an indicated disorder comprising the particular disorder of the matching entry.

Another aspect of the invention encompasses a method for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder in a mammal. The method generally comprises providing a test sample comprising a sample of bodily fluid taken from the mammal. Then the method comprises determining the concentrations of three or more sample analytes in a panel of biomarkers in the test sample, wherein the sample analytes are selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. Diagnostic analytes are identified in the test sample, wherein the diagnostic analytes are the sample analytes whose concentrations are statistically different from concentrations found in a control group of humans who do not suffer from diabetic nephropathy or an associated disorder. The combination of diagnostic analytes is compared to a dataset comprising at least one entry, wherein each entry of the dataset comprises a combination of three or more diagnostic analytes reflective of diabetic nephropathy or an associated disorder. The particular disorder having the combination of diagnostic analytes that essentially match the combination of sample analytes is then identified.

An additional aspect of the invention encompasses a method for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder in a mammal. The method usually comprises providing an analyte concentration measurement device comprising three or more detection antibodies. Each detection antibody comprises an antibody coupled to an indicator, wherein the antigenic determinants of the antibodies are sample analytes associated with diabetic nephropathy or an associated disorder. The sample analytes are generally selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. The method next comprises providing a test sample comprising three or more sample analytes and a bodily fluid taken from the mammal. The test sample is contacted with the detection antibodies and the detection antibodies are allowed to bind to the sample analytes. The concentrations of the sample analytes are determined by detecting the indicators of the detection antibodies bound to the sample analytes in the test sample. The concentrations of each sample analyte correspond to a corresponding minimum diagnostic concentration reflective of diabetic nephropathy or an associated disorder.

Other aspects and iterations of the invention are described in more detail below.

DESCRIPTION OF FIGURES

FIG. 1 shows the four different disease groups from which samples were analyzed, and a plot of two different estimations on eGFR outlining the distribution within each group.

FIG. 2A is a number of scatter plots of results on selected proteins in urine and plasma. The various groups are indicated as follows—control: blue, AA: red, DN: green, GN: yellow, OU: orange. (A) A1M in plasma, (B) cystatin C in plasma,

FIG. 2B is a number of scatter plots of results on selected proteins in urine and plasma. The various groups are indicated as follows—control: blue, AA: red, DN: green, GN: yellow, OU: orange. (C) B2M in urine, (D) cystatin C in urine.

FIG. 3 depicts the multivariate analysis of the disease groups and their respective matched controls using plasma results. Relative importance shown using the random forest model.

FIG. 4A depicts a graph showing the mean AUROC and its standard deviation for plasma samples, and mean error rates

FIG. 4B depicts a graph showing the mean AUROC and its standard deviation and mean AUROC

FIG. 4C depicts a graph showing the mean AUROC and its standard deviation from urine samples for each classification method used to distinguish disease samples vs. normal samples. Disease encompasses analgesic abuse (AA), glomerulonephritis (GN), obstructive uropathy (OU), and diabetic nephropathy (DN). Normal=NL.

FIG. 5A depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 5A and FIG. 5B) or boosting (FIG. 5C) to distinguish disease (AA+GN+ON+DN) samples vs. normal samples from plasma (FIG. 5A) and urine (FIG. 5B and FIG. 5C).

FIG. 5B depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 5A and FIG. 5B) or boosting (FIG. 5C) to distinguish disease (AA+GN+ON+DN) samples vs. normal samples from plasma (FIG. 5A) and urine (FIG. 5B and FIG. 5C).

FIG. 5C depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 5A and FIG. 5B) or boosting (FIG. 5C) to distinguish disease (AA+GN+ON+DN) samples vs. normal samples from plasma (FIG. 5A) and urine (FIG. 5B and FIG. 5C).

FIG. 6A depicts a graph showing the mean AUROC and its standard deviation for plasma samples, and mean error rates

FIG. 6B depicts a graph showing the mean AUROC and its standard deviation and mean AUROC

FIG. 6C depicts a graph showing the mean AUROC and its standard deviation from urine samples for each classification method used to distinguish diabetic nephropathy samples vs. normal samples. Abbreviations as in FIG. 4.

FIG. 7A depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 7A and FIG. 7B) or boosting (FIG. 7C) to distinguish diabetic nephropathy samples vs. normal samples from plasma (FIG. 7A) and urine (FIG. 7B and FIG. 7C).

FIG. 7B depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 7A and FIG. 7B) or boosting (FIG. 7C) to distinguish diabetic nephropathy samples vs. normal samples from plasma (FIG. 7A) and urine (FIG. 7B and FIG. 7C).

FIG. 7C depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 7A and FIG. 7B) or boosting (FIG. 7C) to distinguish diabetic nephropathy samples vs. normal samples from plasma (FIG. 7A) and urine (FIG. 7B and FIG. 7C).

FIG. 8A depicts a graph showing the mean AUROC and its standard deviation for plasma samples, and mean error rates

FIG. 8B depicts a graph showing the mean AUROC and its standard deviation and mean AUROC

FIG. 8C depicts a graph showing the mean AUROC and its standard deviation from urine samples for each classification method used to distinguish analgesic abuse samples vs. diabetic nephropathy samples. Abbreviations as in FIG. 4.

FIG. 9A depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 9A and FIG. 9B) or boosting (FIG. 9C) to distinguish analgesic abuse samples vs. diabetic nephropathy samples from plasma (FIG. 9A) and urine (FIG. 9B and FIG. 9C).

FIG. 9B depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 9A and FIG. 9B) or boosting (FIG. 9C) to distinguish analgesic abuse samples vs. diabetic nephropathy samples from plasma (FIG. 9A) and urine (FIG. 9B and FIG. 9C).

FIG. 9C depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 9A and FIG. 9B) or boosting (FIG. 9C) to distinguish analgesic abuse samples vs. diabetic nephropathy samples from plasma (FIG. 9A) and urine (FIG. 9B and FIG. 9C).

FIG. 10A depicts a graph showing the mean AUROC and its standard deviation for plasma samples, and mean error rates

FIG. 10B depicts a graph showing the mean AUROC and its standard deviation and mean AUROC

FIG. 10C depicts a graph showing the mean AUROC and its standard deviation from urine samples for each classification method used to distinguish obstructive uropathy samples vs. diabetic nephropathy samples. Abbreviations as in FIG. 4.

FIG. 11A depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 11A and FIG. 11B) or boosting (FIG. 11C) to distinguish obstructive uropathy samples vs. diabetic nephropathy samples from plasma (FIG. 11A) and urine (FIG. 11B and FIG. 11C).

FIG. 11B depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 11A and FIG. 11B) or boosting (FIG. 11C) to distinguish obstructive uropathy samples vs. diabetic nephropathy samples from plasma (FIG. 11A) and urine (FIG. 11B and FIG. 11C).

FIG. 11C depicts a graph showing the average importance of analytes and clinical variables from 100 bootstrap runs measured by random forest (FIG. 11A and FIG. 11B) or boosting (FIG. 11C) to distinguish obstructive uropathy samples vs. diabetic nephropathy samples from plasma (FIG. 11A) and urine (FIG. 11B and FIG. 11C).

FIG. 12A depicts a graph showing the mean AUROC and its standard deviation for plasma samples, and mean error rates

FIG. 12B depicts a graph showing the mean AUROC and its standard deviation and mean AUROC

FIG. 12C depicts a graph showing the mean AUROC and its standard deviation from urine samples for each classification method used to distinguish diabetic nephropathy samples vs. glomerulonephritis samples. Abbreviations as in FIG. 4.

FIG. 13A depicts a graph showing the average importance of analytes and clinical variables from I 00 bootstrap runs measured by random forest (FIG. 13A and FIG. 13B) or boosting (FIG. 13C) to distinguish diabetic nephropathy samples vs. glomerulonephritis samples from plasma (FIG. 13A) and urine (FIG. 13B and FIG. 13C).

FIG. 13B depicts a graph showing the average importance of analytes and clinical variables from I 00 bootstrap runs measured by random forest (FIG. 13A and FIG. 13B) or boosting (FIG. 13C) to distinguish diabetic nephropathy samples vs. glomerulonephritis samples from plasma (FIG. 13A) and urine (FIG. 13B and FIG. 13C).

FIG. 13C depicts a graph showing the average importance of analytes and clinical variables from I 00 bootstrap runs measured by random forest (FIG. 13A and FIG. 13B) or boosting (FIG. 13C) to distinguish diabetic nephropathy samples vs. glomerulonephritis samples from plasma (FIG. 13A) and urine (FIG. 13B and FIG. 13C).

FIG. 14A depicts several graphs illustrating the linear correlation between an analyte and years diagnosed with diabetes. Red=cases; Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C) calbindin, (D) clusteri; FIG. 14B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 14C: (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 14D (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 14B depicts several graphs illustrating the linear correlation between an analyte and years diagnosed with diabetes. Red=cases; Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C) calbindin, (D) clusteri; FIG. 14B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 14C: (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 14D (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 14C depicts several graphs illustrating the linear correlation between an analyte and years diagnosed with diabetes. Red=cases; Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C) calbindin, (D) clusteri; FIG. 14B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 14C: (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 14D (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 14D depicts several graphs illustrating the linear correlation between an analyte and years diagnosed with diabetes. Red=cases; Black=controls. FIG. 14A: (A) A1M, (B) B2M, (C) calbindin, (D) clusteri; FIG. 14B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 14C: (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 14D (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 15A depicts several graphs illustrating the log correlation between an analyte and years diagnosed with diabetes. Red=cases; Black=controls. FIG. 15A: (A) A1M, (B) B2M, (C) calbindin, (D) clusterin; FIG. 15B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 15C: (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 15B depicts several graphs illustrating the log correlation between an analyte and years diagnosed with diabetes. Red=cases; Black=controls. FIG. 15A: (A) A1M, (B) B2M, (C) calbindin, (D) clusterin; FIG. 15B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 15C: (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 15C depicts several graphs illustrating the log correlation between an analyte and years diagnosed with diabetes. Red=cases; Black=controls. FIG. 15A: (A) A1M, (B) B2M, (C) calbindin, (D) clusterin; FIG. 15B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 15C: (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 15D depicts several graphs illustrating the log correlation between an analyte and years diagnosed with diabetes. Red=cases; Black=controls. FIG. 15A: (A) A1M, (B) B2M, (C) calbindin, (D) clusterin; FIG. 15B: (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α; FIG. 15C: (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin; FIG. 15D: (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 16A depicts several graphs illustrating the log correlation between an analyte and clinical 24 hr microalbumin (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 16B depicts several graphs illustrating the log correlation between an analyte and clinical 24 hr microalbumin (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 16C depicts several graphs illustrating the log correlation between an analyte and clinical 24 hr microalbumin (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 16D depicts several graphs illustrating the log correlation between an analyte and clinical 24 hr microalbumin (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 17 A depicts several graphs illustrating the linear correlation between an analyte and clinical 24 hr microalbumin. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 17B depicts several graphs illustrating the linear correlation between an analyte and clinical 24 hr microalbumin. (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 17C depicts several graphs illustrating the linear correlation between an analyte and clinical 24 hr microalbumin. (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin

FIG. 17D depicts several graphs illustrating the linear correlation between an analyte and clinical 24 hr microalbumin. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 18A depicts several graphs illustrating linear cdplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 18B depicts several graphs illustrating linear cdplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 18C depicts several graphs illustrating linear cdplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 18D depicts several graphs illustrating linear cdplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 19A depicts several graphs illustrating log cdplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 19B depicts several graphs illustrating log cdplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 19C depicts several graphs illustrating log cdplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 19D depicts several graphs illustrating log cdplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 20A depicts several graphs illustrating linear qqplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 20B depicts several graphs illustrating linear qqplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 20C depicts several graphs illustrating linear qqplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 20D depicts several graphs illustrating linear qqplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 21A depicts several graphs illustrating log qqplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin;

FIG. 21B depicts several graphs illustrating log qqplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (E) CTGF, (F) creatinine, (G) cystatin C, (H) GST α;

FIG. 21C depicts several graphs illustrating log qqplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 21D depicts several graphs illustrating log qqplots of urine analytes compared to diabetic disease. Levels were normalized to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 22A depicts several graphs illustrating linear stripcharts of urine analytes compared to diabetic kidney disease (KD) or diabetic patients without kidney disease controls (NC). Levels were normalized to urine creatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin, (E) CTGF, (F) creatinine;

FIG. 22B depicts several graphs illustrating linear stripcharts of urine analytes compared to diabetic kidney disease (KD) or diabetic patients without kidney disease controls (NC). Levels were normalized to urine creatinine. (G) cystatin C, (H) GST α, (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 22C depicts several graphs illustrating linear stripcharts of urine analytes compared to diabetic kidney disease (KD) or diabetic patients without kidney disease controls (NC). Levels were normalized to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 23A depicts several graphs illustrating log stripcharts of urine analytes compared to diabetic kidney disease (KD) or diabetic patients without kidney disease controls (NC). Levels were normalized to urine creatinine. (A) A1M, (B) B2M, (C) calbindin, (D) clusterin, (E) CTGF, (F) creatinine;

FIG. 23B depicts several graphs illustrating log stripcharts of urine analytes compared to diabetic kidney disease (KD) or diabetic patients without kidney disease controls (NC). Levels were normalized to urine creatinine. (G) cystatin C, (H) GST α, (I) KIM-I, (J) microalbumin, (K) NGAL, (L) osteopontin;

FIG. 23C depicts several graphs illustrating log stripcharts of urine analytes compared to diabetic kidney disease (KD) or diabetic patients without kidney disease controls (NC). Levels were normalized to urine creatinine. (M) THP, (N) TIMP-1, (O) TFF-3, and (P) VEGF.

FIG. 24 depicts a graph illustrating years diagnosed v. disease.

FIG. 25A depicts several graphs illustrating linear stripcharts of serum analytes compared to diabetic kidney disease (KD) or diabetic patients without kidney disease controls (NC). (A) A1M, (B) B2M, (C) clusterin, (D) CTGF, (E) cystatin C, (F) GST α;

FIG. 25B depicts several graphs illustrating linear stripcharts of serum analytes compared to diabetic kidney disease (KD) or diabetic patients without kidney disease controls (NC). (G) KIM-I, (H) NGAL, (I) osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1; and

FIG. 25C depicts a graph illustrating linear stripcharts of serum analytes compared to diabetic kidney disease (KD) or diabetic patients without kidney disease controls (NC). (M) VEGF.

FIG. 26A depicts several graphs illustrating log stripcharts of serum analytes compared to diabetic kidney disease. (A) A1M, (B) B2M;

FIG. 26B depicts several graphs illustrating log stripcharts of serum analytes compared to diabetic kidney disease. (C) clusterin, (D) CTGF, (E) cystatin C, (F) GST α, (G) KIM-I, (H) NGAL;

FIG. 26C depicts several graphs illustrating log stripcharts of serum analytes compared to diabetic kidney disease. (I) osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1, and (M) VEGF.

FIG. 27A depicts several graphs illustrating linear qqplots of serum analytes compared to diabetic kidney disease. (A) A1M, (B) B2M, (C) clusterin, (D) CTGF;

FIG. 27B depicts several graphs illustrating linear qqplots of serum analytes compared to diabetic kidney disease. (E) cystatin C, (F) GST α, (G) KIM-I, (H) NGAL;

FIG. 27C depicts several graphs illustrating linear qqplots of serum analytes compared to diabetic kidney disease. (I) osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1; and

FIG. 27D depicts a graph illustrating linear qqplots of serum analytes compared to diabetic kidney disease. (M) VEGF.

FIG. 28A depicts several graphs illustrating log qqplots of serum analytes compared to diabetic kidney disease. (A) A1M, (B) B2M;

FIG. 28B depicts several graphs illustrating log qqplots of serum analytes compared to diabetic kidney disease. (C) clusterin, (D) CTGF, (E) cystatin C, (F) GST α;

FIG. 28C depicts several graphs illustrating log qqplots of serum analytes compared to diabetic kidney disease. (G) KIM-I, (H) NGAL, (I) osteopontin, (J) TFF-3:

FIG. 28D depicts several graphs illustrating log qqplots of serum analytes compared to diabetic kidney disease. (K) THP, (L) TIMP-1, and (M) VEGF.

FIG. 29A depicts several graphs illustrating a linear comparison of analytes v. years diagnosed. Red=cases; Black=controls. (A) A1M, (B) B2M, (C) clusterin, (D) CTGF;

FIG. 29B depicts several graphs illustrating a linear comparison of analytes v. years diagnosed. Red=cases; Black=controls. (E) cystatinC, (F) GST α, (G) KIM-I, (H) NGAL;

FIG. 29C depicts several graphs illustrating a linear comparison of analytes v. years diagnosed. Red=cases; Black=controls. (I) osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1, and (M) VEGF.

FIG. 30A depicts several graphs illustrating a log comparison of analytes v. years diagnosed. Red=cases; Black=controls. (A) A1M, (B) B2M, (C) clusterin, (D) CTGF;

FIG. 30B depicts several graphs illustrating a log comparison of analytes v. years diagnosed. Red=cases; Black=controls. (E) cystatin C, (F) GST α, (G) KIM-I, (H) NGAL;

FIG. 30C depicts several graphs illustrating a log comparison of analytes v. years diagnosed. Red=cases; Black=controls. (I) osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1, and (M) VEGF.

FIG. 31A depicts several graphs illustrating a linear comparison of serum analytes v. clinical microalbumin. (A) A1M, (B) B2M, (C) clusterin, (D) CTGF;

FIG. 31B depicts several graphs illustrating a linear comparison of serum analytes v. clinical microalbumin. (E) cystatin C, (F) GST α, (G) KIM-I, (H) NGAL

FIG. 31C depicts several graphs illustrating a linear comparison of serum analytes v. clinical microalbumin. (I) osteopontin, (J) TFF-3, (K) THP, (L) TIMP-1; and

FIG. 31D depicts a graph illustrating a linear comparison of serum analytes v. clinical microalbumin. (M) VEGF.

FIG. 32A depicts several graphs illustrating a log comparison of serum analytes v. clinical microalbumin. (A) A1M, (B) B2M;

FIG. 32B depicts several graphs illustrating a log comparison of serum analytes v. clinical microalbumin. (C) clusterin, (D) CTGF, (E) cystatin C, (F) GST α;

FIG. 32C depicts several graphs illustrating a log comparison of serum analytes v. clinical microalbumin. (G) KIM-I, (H) NGAL, (I) osteopontin, (J) TFF-3;

FIG. 32D depicts several graphs illustrating a log comparison of serum analytes v. clinical microalbumin. (K) THP, (L) TIMP-1, and (M) VEGF.

DETAILED DESCRIPTION OF THE INVENTION

It has been discovered that a multiplexed panel of at least three, six, or preferably 16 biomarkers may be used to detect diabetic nephropathy and associated disorders. As used herein, the term “diabetic nephropathy” refers to a disorder characterized by angiopathy of capillaries in the kidney glomeruli. The term encompasses Kimmelstiel-Wilson syndrome, or nodular diabetic glomerulosclerosis and intercapillary glomerulonephritis. Additionally, the present invention encompasses biomarkers that may be used to detect a disorder associated with diabetic nephropathy. As used herein, the phrase “a disorder associated with diabetic nephropathy” refers to a disorder that stems from angiopathy of capillaries in the kidney glomeruli. For instance, non-limiting examples of associated disorders may include nephritic syndrome, chronic kidney failure, and end-stage kidney disease.

The biomarkers included in a multiplexed panel of the invention are analytes known in the art that may be detected in the urine, serum, plasma and other bodily fluids of mammals. As such, the analytes of the multiplexed panel may be readily extracted from the mammal in a test sample of bodily fluid. The concentrations of the analytes within the test sample may be measured using known analytical techniques such as a multiplexed antibody-based immunological assay. The combination of concentrations of the analytes in the test sample may be compared to empirically determined combinations of minimum diagnostic concentrations and combinations of diagnostic concentration ranges associated with healthy kidney function or diabetic nephropathy or an associated disorder to determine whether diabetic nephropathy or an associated disorder is indicated in the mammal.

One embodiment of the present invention provides a method for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder in a mammal that includes determining the presence or concentration of a combination of three or more sample analytes in a test sample containing the bodily fluid of the mammal. The measured concentrations of the combination of sample analytes is compared to the entries of a dataset in which each entry contains the minimum diagnostic concentrations of a combination of three of more analytes reflective of diabetic nephropathy or an associated disorder. Other embodiments provide computer-readable media encoded with applications containing executable modules, systems that include databases and processing devices containing executable modules configured to diagnose, monitor, or determine a renal disorder in a mammal. Still other embodiments provide antibody-based devices for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder in a mammal.

The analytes used as biomarkers in the multiplexed assay, methods of diagnosing, monitoring, or determining a renal disorder using measurements of the analytes, systems and applications used to analyze the multiplexed assay measurements, and antibody-based devices used to measure the analytes are described in detail below.

I. Analytes in Multiplexed Assay

One embodiment of the invention measures the concentrations of three, six, or preferable sixteen biomarker analytes within a test sample taken from a mammal and compares the measured analyte concentrations to minimum diagnostic concentrations to diagnose, monitor, or determine diabetic nephropathy or an associated disorder in a mammal. In this aspect, the biomarker analytes are known in the art to occur in the urine, plasma, serum and other bodily fluids of mammals. The biomarker analytes are proteins that have known and documented associations with early renal damage in humans. As defined herein, the biomarker analytes include but are not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. A description of each biomarker analyte is given below.

(a) Alpha-1 Microglobulin (A1M)

Alpha-1 microglobulin (A1M, Swiss-Prot Accession Number P02760) is a 26 kDa glycoprotein synthesized by the liver and reabsorbed in the proximal tubules. Elevated levels of A1M in human urine are indicative of glomerulotubular dysfunction. A1M is a member of the lipocalin super family and is found in all tissues. Alpha-1-microglobulin exists in blood in both a free form and complexed with immunoglobulin A (IgA) and heme. Half of plasma A1M exists in a free form, and the remainder exists in complexes with other molecules including prothrombin, albumin, immunoglobulin A and heme. Nearly all of the free A1M in human urine is reabsorbed by the megalin receptor in proximal tubular cells, where it is then catabolized. Small amounts of A1M are excreted in the urine of healthy humans. Increased A1M concentrations in human urine may be an early indicator of renal damage, primarily in the proximal tubule.

(b) Beta-2 Microglobulin (B2M)

Beta-2 microglobulin (B2M, Swiss-Prot Accession Number P61769) is a protein found on the surfaces of all nucleated cells and is shed into the blood, particularly by tumor cells and lymphocytes. Due to its small size, B2M passes through the glomerular membrane, but normally less than 1% is excreted due to reabsorption of B2M in the proximal tubules of the kidney. Therefore, high plasma levels of B2M occur as a result of renal failure, inflammation, and neoplasms, especially those associated with B-lymphocytes.

(c) Calbindin

Calbindin (Calbindin D-28K, Swiss-Prot Accession Number P05937) is a Ca-binding protein belonging to the troponin C superfamily. It is expressed in the kidney, pancreatic islets, and brain. Calbindin is found predominantly in subpopulations of central and peripheral nervous system neurons, in certain epithelial cells involved in Ca2+ transport such as distal tubular cells and cortical collecting tubules of the kidney, and in enteric neuroendocrine cells.

(d) Clusterin

Clusterin (Swiss-Prot Accession Number P10909) is a highly conserved protein that has been identified independently by many different laboratories and named SGP2, S35-S45, apolipoprotein J, SP-40, 40, ADHC-9, gp80, GPIII, and testosterone-repressed prostate message (TRPM-2). An increase in clusterin levels has been consistently detected in apoptotic heart, brain, lung, liver, kidney, pancreas, and retinal tissue both in vivo and in vitro, establishing clusterin as a ubiquitous marker of apoptotic cell loss. However, clusterin protein has also been implicated in physiological processes that do not involve apoptosis, including the control of complement-mediated cell lysis, transport of beta-amyloid precursor protein, shuttling of aberrant beta-amyloid across the blood-brain barrier, lipid scavenging, membrane remodeling, cell aggregation, and protection from immune detection and tumor necrosis factor induced cell death.

(e) Connective Tissue Growth Factor (CTGF)

Connective tissue growth factor (CTGF, Swiss-Prot Accession Number P29279) is a 349-amino acid cysteine-rich polypeptide belonging to the KCN family. In vitro studies have shown that CTGF is mainly involved in extracellular matrix synthesis and fibrosis. Up-regulation of CTGF mRNA and increased CTGF levels have been observed in various diseases, including diabetic nephropathy and cardiomyopathy, fibrotic skin disorders, systemic sclerosis, biliary atresia, liver fibrosis and idiopathic pulmonary fibrosis, and nondiabetic acute and progressive glomerular and tubulointerstitial lesions of the kidney. A recent cross-sectional study found that urinary CTGF may act as a progression promoter in diabetic nephropathy.

(f) Creatinine

Creatinine is a metabolite of creatine phosphate in muscle tissue, and is typically produced at a relatively constant rate by the body. Creatinine is chiefly filtered out of the blood by the kidneys, though a small amount is actively secreted by the kidneys into the urine. Creatinine levels in blood and urine may be used to estimate the creatinine clearance, which is representative of the overall glomerular filtration rate (GFR), a standard measure of renal function. Variations in creatinine concentrations in the blood and urine, as well as variations in the ratio of urea to creatinine concentration in the blood, are common diagnostic measurements used to assess renal function.

(g) Cystatin C (Cyst C)

Cystatin C (Cyst C, Swiss-Prot Accession Number P01034) is a 13 kDa protein that is a potent inhibitor of the Cl family of cysteine proteases. It is the most abundant extracellular inhibitor of cysteine proteases in testis, epididymis, prostate, seminal vesicles and many other tissues. Cystatin C, which is normally expressed in vascular wall smooth muscle cells, is severely reduced in both atherosclerotic and aneurismal aortic lesions.

(h) Glutathione S-Transferase Alpha (GST-Alpha)

Glutathione S-transferase alpha (GST-alpha, Swiss-Prot Accession Number P08263) belongs to a family of enzymes that utilize glutathione in reactions contributing to the transformation of a wide range of compounds, including carcinogens, therapeutic drugs, and products of oxidative stress. These enzymes play a key role in the detoxification of such substances.

(i) Kidney Injury Molecule-1 (KIM-1)

Kidney injury molecule-1 (KIM-1, Swiss-Prot Accession Number Q96D42) is an immunoglobulin superfamily cell-surface protein highly upregulated on the surface of injured kidney epithelial cells. It is also known as TIM-1 (T-cell immunoglobulin mucin domain-1), as it is expressed at low levels by subpopulations of activated T-cells and hepatitis A virus cellular receptor-1 (HAVCR-1). KIM-1 is increased in expression more than any other protein in the injured kidney and is localized predominantly to the apical membrane of the surviving proximal epithelial cells.

(j) Microalbumin

Albumin is the most abundant plasma protein in humans and other mammals. Albumin is essential for maintaining the osmotic pressure needed for proper distribution of body fluids between intravascular compartments and body tissues. Healthy, normal kidneys typically filter out albumin from the urine. The presence of albumin in the urine may indicate damage to the kidneys. Albumin in the urine may also occur in patients with long-standing diabetes, especially type 1 diabetes. The amount of albumin eliminated in the urine has been used to differentially diagnose various renal disorders. For example, nephrotic syndrome usually results in the excretion of about 3.0 to 3.5 grams of albumin in human urine every 24 hours. Microalbuminuria, in which less than 300 mg of albumin is eliminated in the urine every 24 hours, may indicate the early stages of diabetic nephropathy.

(k) Neutrophil Gelatinase-Associated Lipocalin (NGAL)

Neutrophil gelatinase-associated lipocalin (NGAL, Swiss-Prot Accession Number P80188) forms a disulfide bond-linked heterodimer with MMP-9. It mediates an innate immune response to bacterial infection by sequestrating iron. Lipocalins interact with many different molecules such as cell surface receptors and proteases, and play a role in a variety of processes such as the progression of cancer and allergic reactions.

(l) Osteopontin (OPN)

Osteopontin (OPN, Swiss-Prot Accession Number P10451) is a cytokine involved in enhancing production of interferon-gamma and IL-12, and inhibiting the production of IL-10. OPN is essential in the pathway that leads to type I immunity. OPN appears to form an integral part of the mineralized matrix. OPN is synthesized within the kidney and has been detected in human urine at levels that may effectively inhibit calcium oxalate crystallization. Decreased concentrations of OPN have been documented in urine from patients with renal stone disease compared with normal individuals.

(m) Tamm-Horsfall Protein (THP)

Tamm-Horsfall protein (THP, Swiss-Prot Accession Number P07911), also known as uromodulin, is the most abundant protein present in the urine of healthy subjects and has been shown to decrease in individuals with kidney stones. THP is secreted by the thick ascending limb of the loop of Henley. THP is a monomeric glycoprotein of ˜85 kDa with ˜30% carbohydrate moiety that is heavily glycosylated. THP may act as a constitutive inhibitor of calcium crystallization in renal fluids.

(n) Tissue Inhibitor of Metalloproteinase-1 (TIMP-1)

Tissue inhibitor of metalloproteinase-1 (TIMP-1, Swiss-Prot Accession Number P01033) is a major regulator of extracellular matrix synthesis and degradation. A certain balance of MMPs and TIMPs is essential for tumor growth and health. Fibrosis results from an imbalance of fibrogenesis and fibrolysis, highlighting the importance of the role of the inhibition of matrix degradation role in renal disease.

(o) Trefoil Factor 3 (TFF3)

Trefoil factor 3 (TFF3, Swiss-Prot Accession Number Q07654), also known as intestinal trefoil factor, belongs to a small family of mucin-associated peptides that include TFF1, TFF2, and TFF3. TFF3 exists in a 60-amino acid monomeric form and a 118-amino acid dimeric form. Under normal conditions TFF3 is expressed by goblet cells of the intestine and the colon. TFF3 expression has also been observed in the human respiratory tract, in human goblet cells and in the human salivary gland. In addition, TFF3 has been detected in the human hypothalamus.

(p) Vascular Endothelial Growth Factor (VEGF)

Vascular endothelial growth factor (VEGF, Swiss-Prot Accession Number P15692) is an important factor in the pathophysiology of neuronal and other tumors, most likely functioning as a potent promoter of angiogenesis. VEGF may also be involved in regulating blood-brain-barrier functions under normal and pathological conditions. VEGF secreted from the stromal cells may be responsible for the endothelial cell proliferation observed in capillary hemangioblastomas, which are typically composed of abundant microvasculature and primitive angiogenic elements represented by stromal cells.

II. Combinations of Analytes Measured by Multiplexed Assay

The method for diagnosing, monitoring, or determining a renal disorder involves determining the presence or concentrations of a combination of sample analytes in a test sample. The combinations of sample analytes, as defined herein, are any group of three or more analytes selected from the biomarker analytes, including but not limited to alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. In one embodiment, the combination of analytes may be selected to provide a group of analytes associated with diabetic nephropathy or an associated disorder.

In one embodiment, the combination of sample analytes may be any three of the biomarker analytes. In other embodiments, the combination of sample analytes may be any four, any five, any six, any seven, any eight, any nine, any ten, any eleven, any twelve, any thirteen, any fourteen, any fifteen, or all sixteen of the sixteen biomarker analytes. In some embodiments, the combination of sample analytes comprises alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1. In another embodiment, the combination of sample analytes may be a combination listed in Table A.

TABLE A alpha-1 microglobulin beta-2 microglobulin calbindin alpha-1 microglobulin beta-2 microglobulin clusterin alpha-1 microglobulin beta-2 microglobulin CTGF alpha-1 microglobulin beta-2 microglobulin creatinine alpha-1 microglobulin beta-2 microglobulin cystatin C alpha-1 microglobulin beta-2 microglobulin GST-alpha alpha-1 microglobulin beta-2 microglobulin KIM-1 alpha-1 microglobulin beta-2 microglobulin microalbumin alpha-1 microglobulin beta-2 microglobulin NGAL alpha-1 microglobulin beta-2 microglobulin osteopontin alpha-1 microglobulin beta-2 microglobulin THP alpha-1 microglobulin beta-2 microglobulin TIMP-1 alpha-1 microglobulin beta-2 microglobulin TFF-3 alpha-1 microglobulin beta-2 microglobulin VEGF alpha-1 microglobulin calbindin clusterin alpha-1 microglobulin calbindin CTGF alpha-1 microglobulin calbindin creatinine alpha-1 microglobulin calbindin cystatin C alpha-1 microglobulin calbindin GST-alpha alpha-1 microglobulin calbindin KIM-1 alpha-1 microglobulin calbindin microalbumin alpha-1 microglobulin calbindin NGAL alpha-1 microglobulin calbindin osteopontin alpha-1 microglobulin calbindin THP alpha-1 microglobulin calbindin TIMP-1 alpha-1 microglobulin calbindin TFF-3 alpha-1 microglobulin calbindin VEGF alpha-1 microglobulin clusterin CTGF alpha-1 microglobulin clusterin creatinine alpha-1 microglobulin clusterin cystatin C alpha-1 microglobulin clusterin GST-alpha alpha-1 microglobulin clusterin KIM-1 alpha-1 microglobulin clusterin microalbumin alpha-1 microglobulin clusterin NGAL alpha-1 microglobulin clusterin osteopontin alpha-1 microglobulin clusterin THP alpha-1 microglobulin clusterin TIMP-1 alpha-1 microglobulin clusterin TFF-3 alpha-1 microglobulin clusterin VEGF alpha-1 microglobulin CTGF creatinine alpha-1 microglobulin CTGF cystatin C alpha-1 microglobulin CTGF GST-alpha alpha-1 microglobulin CTGF KIM-1 alpha-1 microglobulin CTGF microalbumin alpha-1 microglobulin CTGF NGAL alpha-1 microglobulin CTGF osteopontin alpha-1 microglobulin CTGF THP alpha-1 microglobulin CTGF TIMP-1 alpha-1 microglobulin CTGF TFF-3 alpha-1 microglobulin CTGF VEGF alpha-1 microglobulin creatinine cystatin C alpha-1 microglobulin creatinine GST-alpha alpha-1 microglobulin creatinine KIM-1 alpha-1 microglobulin creatinine microalbumin alpha-1 microglobulin creatinine NGAL alpha-1 microglobulin creatinine osteopontin alpha-1 microglobulin creatinine THP alpha-1 microglobulin creatinine TIMP-1 alpha-1 microglobulin creatinine TFF-3 alpha-1 microglobulin creatinine VEGF alpha-1 microglobulin cystatin C GST-alpha alpha-1 microglobulin cystatin C KIM-1 alpha-1 microglobulin cystatin C microalbumin alpha-1 microglobulin cystatin C NGAL alpha-1 microglobulin cystatin C osteopontin alpha-1 microglobulin cystatin C THP alpha-1 microglobulin cystatin C TIMP-1 alpha-1 microglobulin cystatin C TFF-3 alpha-1 microglobulin cystatin C VEGF alpha-1 microglobulin GST-alpha KIM-1 alpha-1 microglobulin GST-alpha microalbumin alpha-1 microglobulin GST-alpha NGAL alpha-1 microglobulin GST-alpha osteopontin alpha-1 microglobulin GST-alpha THP alpha-1 microglobulin GST-alpha TIMP-1 alpha-1 microglobulin GST-alpha TFF-3 alpha-1 microglobulin GST-alpha VEGF alpha-1 microglobulin KIM-1 microalbumin alpha-1 microglobulin KIM-1 NGAL alpha-1 microglobulin KIM-1 osteopontin alpha-1 microglobulin KIM-1 THP alpha-1 microglobulin KIM-1 TIMP-1 alpha-1 microglobulin KIM-1 TFF-3 alpha-1 microglobulin KIM-1 VEGF alpha-1 microglobulin microalbumin NGAL alpha-1 microglobulin microalbumin osteopontin alpha-1 microglobulin microalbumin THP alpha-1 microglobulin microalbumin TIMP-1 alpha-1 microglobulin microalbumin TFF-3 alpha-1 microglobulin microalbumin VEGF alpha-1 microglobulin NGAL osteopontin alpha-1 microglobulin NGAL THP alpha-1 microglobulin NGAL TIMP-1 alpha-1 microglobulin NGAL TFF-3 alpha-1 microglobulin NGAL VEGF alpha-1 microglobulin osteopontin THP alpha-1 microglobulin osteopontin TIMP-1 alpha-1 microglobulin osteopontin TFF-3 alpha-1 microglobulin osteopontin VEGF alpha-1 microglobulin THP TIMP-1 alpha-1 microglobulin THP TFF-3 alpha-1 microglobulin THP VEGF alpha-1 microglobulin TIMP-1 TFF-3 alpha-1 microglobulin TIMP-1 VEGF alpha-1 microglobulin TFF-3 VEGF beta-2 microglobulin calbindin clusterin beta-2 microglobulin calbindin CTGF beta-2 microglobulin calbindin creatinine beta-2 microglobulin calbindin cystatin C beta-2 microglobulin calbindin GST-alpha beta-2 microglobulin calbindin KIM-1 beta-2 microglobulin calbindin microalbumin beta-2 microglobulin calbindin NGAL beta-2 microglobulin calbindin osteopontin beta-2 microglobulin calbindin THP beta-2 microglobulin calbindin TIMP-1 beta-2 microglobulin calbindin TFF-3 beta-2 microglobulin calbindin VEGF beta-2 microglobulin clusterin CTGF beta-2 microglobulin clusterin creatinine beta-2 microglobulin clusterin cystatin C beta-2 microglobulin clusterin GST-alpha beta-2 microglobulin clusterin KIM-1 beta-2 microglobulin clusterin microalbumin beta-2 microglobulin clusterin NGAL beta-2 microglobulin clusterin osteopontin beta-2 microglobulin clusterin THP beta-2 microglobulin clusterin TIMP-1 beta-2 microglobulin clusterin TFF-3 beta-2 microglobulin clusterin VEGF beta-2 microglobulin CTGF creatinine beta-2 microglobulin CTGF cystatin C beta-2 microglobulin CTGF GST-alpha beta-2 microglobulin CTGF KIM-1 beta-2 microglobulin CTGF microalbumin beta-2 microglobulin CTGF NGAL beta-2 microglobulin CTGF osteopontin beta-2 microglobulin CTGF THP beta-2 microglobulin CTGF TIMP-1 beta-2 microglobulin CTGF TFF-3 beta-2 microglobulin CTGF VEGF beta-2 microglobulin creatinine cystatin C beta-2 microglobulin creatinine GST-alpha beta-2 microglobulin creatinine KIM-1 beta-2 microglobulin creatinine microalbumin beta-2 microglobulin creatinine NGAL beta-2 microglobulin creatinine osteopontin beta-2 microglobulin creatinine THP beta-2 microglobulin creatinine TIMP-1 beta-2 microglobulin creatinine TFF-3 beta-2 microglobulin creatinine VEGF beta-2 microglobulin cystatin C GST-alpha beta-2 microglobulin cystatin C KIM-1 beta-2 microglobulin cystatin C microalbumin beta-2 microglobulin cystatin C NGAL beta-2 microglobulin cystatin C osteopontin beta-2 microglobulin cystatin C THP beta-2 microglobulin cystatin C TIMP-1 beta-2 microglobulin cystatin C TFF-3 beta-2 microglobulin cystatin C VEGF beta-2 microglobulin GST-alpha KIM-1 beta-2 microglobulin GST-alpha microalbumin beta-2 microglobulin GST-alpha NGAL beta-2 microglobulin GST-alpha osteopontin beta-2 microglobulin GST-alpha THP beta-2 microglobulin GST-alpha TIMP-1 beta-2 microglobulin GST-alpha TFF-3 beta-2 microglobulin GST-alpha VEGF beta-2 microglobulin KIM-1 microalbumin beta-2 microglobulin KIM-1 NGAL beta-2 microglobulin KIM-1 osteopontin beta-2 microglobulin KIM-1 THP beta-2 microglobulin KIM-1 TIMP-1 beta-2 microglobulin KIM-1 TFF-3 beta-2 microglobulin KIM-1 VEGF beta-2 microglobulin microalbumin NGAL beta-2 microglobulin microalbumin osteopontin beta-2 microglobulin microalbumin THP beta-2 microglobulin microalbumin TIMP-1 beta-2 microglobulin microalbumin TFF-3 beta-2 microglobulin microalbumin VEGF beta-2 microglobulin NGAL osteopontin beta-2 microglobulin NGAL THP beta-2 microglobulin NGAL TIMP-1 beta-2 microglobulin NGAL TFF-3 beta-2 microglobulin NGAL VEGF beta-2 microglobulin osteopontin THP beta-2 microglobulin osteopontin TIMP-1 beta-2 microglobulin osteopontin TFF-3 beta-2 microglobulin osteopontin VEGF beta-2 microglobulin THP TIMP-1 beta-2 microglobulin THP TFF-3 beta-2 microglobulin THP VEGF beta-2 microglobulin TIMP-1 TFF-3 beta-2 microglobulin TIMP-2 VEGF beta-2 microglobulin TFF-3 VEGF calbindin clusterin CTGF calbindin clusterin creatinine calbindin clusterin cystatin C calbindin clusterin GST-alpha calbindin clusterin KIM-1 calbindin clusterin microalbumin calbindin clusterin NGAL calbindin clusterin osteopontin calbindin clusterin THP calbindin clusterin TIMP-1 calbindin clusterin TFF-3 calbindin clusterin VEGF calbindin CTGF creatinine calbindin CTGF cystatin C calbindin CTGF GST-alpha calbindin CTGF KIM-1 calbindin CTGF microalbumin calbindin CTGF NGAL calbindin CTGF osteopontin calbindin CTGF THP calbindin CTGF TIMP-1 calbindin CTGF TFF-3 calbindin CTGF VEGF calbindin creatinine cystatin C calbindin creatinine GST-alpha calbindin creatinine KIM-1 calbindin creatinine microalbumin calbindin creatinine NGAL calbindin creatinine osteopontin calbindin creatinine THP calbindin creatinine TIMP-1 calbindin creatinine TFF-3 calbindin creatinine VEGF calbindin cystatin C GST-alpha calbindin cystatin C KIM-1 calbindin cystatin C microalbumin calbindin cystatin C NGAL calbindin cystatin C osteopontin calbindin cystatin C THP calbindin cystatin C TIMP-1 calbindin cystatin C TFF-3 calbindin cystatin C VEGF calbindin GST-alpha KIM-1 calbindin GST-alpha microalbumin calbindin GST-alpha NGAL calbindin GST-alpha osteopontin calbindin GST-alpha THP calbindin GST-alpha TIMP-1 calbindin GST-alpha TFF-3 calbindin GST-alpha VEGF calbindin KIM-1 microalbumin calbindin KIM-1 NGAL calbindin KIM-1 osteopontin calbindin KIM-1 THP calbindin KIM-1 TIMP-1 calbindin KIM-1 TFF-3 calbindin KIM-1 VEGF calbindin microalbumin NGAL calbindin microalbumin osteopontin calbindin microalbumin THP calbindin microalbumin TIMP-1 calbindin microalbumin TFF-3 calbindin microalbumin VEGF calbindin NGAL osteopontin calbindin NGAL THP calbindin NGAL TIMP-1 calbindin NGAL TFF-3 calbindin NGAL VEGF calbindin osteopontin THP calbindin osteopontin TIMP-1 calbindin osteopontin TFF-3 calbindin osteopontin VEGF calbindin THP TIMP-1 calbindin THP TFF-3 calbindin THP VEGF calbindin TIMP-1 TFF-3 calbindin TIMP-1 VEGF calbindin TFF-3 VEGF clusterin CTGF creatinine clusterin CTGF cystatin C clusterin CTGF GST-alpha clusterin CTGF KIM-1 clusterin CTGF microalbumin clusterin CTGF NGAL clusterin CTGF osteopontin clusterin CTGF THP clusterin CTGF TIMP-1 clusterin CTGF TFF-3 clusterin CTGF VEGF clusterin creatinine cystatin C clusterin creatinine GST-alpha clusterin creatinine KIM-1 clusterin creatinine microalbumin clusterin creatinine NGAL clusterin creatinine osteopontin clusterin creatinine THP clusterin creatinine TIMP-1 clusterin creatinine TFF-3 clusterin creatinine VEGF clusterin cystatin C GST-alpha clusterin cystatin C KIM-1 clusterin cystatin C microalbumin clusterin cystatin C NGAL clusterin cystatin C osteopontin clusterin cystatin C THP clusterin cystatin C TIMP-1 clusterin cystatin C TFF-3 clusterin cystatin C VEGF clusterin GST-alpha KIM-1 clusterin GST-alpha microalbumin clusterin GST-alpha NGAL clusterin GST-alpha osteopontin clusterin GST-alpha THP clusterin GST-alpha TIMP-1 clusterin GST-alpha TFF-3 clusterin GST-alpha VEGF clusterin KIM-1 microalbumin clusterin KIM-1 NGAL clusterin KIM-1 osteopontin clusterin KIM-1 THP clusterin KIM-1 TIMP-1 clusterin KIM-1 TFF-3 clusterin KIM-1 VEGF clusterin microalbumin NGAL clusterin microalbumin osteopontin clusterin microalbumin THP clusterin microalbumin TIMP-1 clusterin microalbumin TFF-3 clusterin microalbumin VEGF clusterin NGAL osteopontin clusterin NGAL THP clusterin NGAL TIMP-1 clusterin NGAL TFF-3 clusterin NGAL VEGF clusterin osteopontin THP clusterin osteopontin TIMP-1 clusterin osteopontin TFF-3 clusterin osteopontin VEGF clusterin THP TIMP-1 clusterin THP TFF-3 clusterin THP VEGF clusterin TIMP-1 TFF-3 clusterin TIMP-1 VEGF clusterin TFF-3 VEGF CTGF creatinine cystatin C CTGF creatinine GST-alpha CTGF creatinine KIM-1 CTGF creatinine microalbumin CTGF creatinine NGAL CTGF creatinine osteopontin CTGF creatinine THP CTGF creatinine TIMP-1 CTGF creatinine TFF-3 CTGF creatinine VEGF CTGF cystatin C GST-alpha CTGF cystatin C KIM-1 CTGF cystatin C microalbumin CTGF cystatin C NGAL CTGF cystatin C osteopontin CTGF cystatin C THP CTGF cystatin C TIMP-1 CTGF cystatin C TFF-3 CTGF cystatin C VEGF CTGF GST-alpha KIM-1 CTGF GST-alpha microalbumin CTGF GST-alpha NGAL CTGF GST-alpha osteopontin CTGF GST-alpha THP CTGF GST-alpha TIMP-1 CTGF GST-alpha TFF-3 CTGF GST-alpha VEGF CTGF KIM-1 microalbumin CTGF KIM-1 NGAL CTGF KIM-1 osteopontin CTGF KIM-1 THP CTGF KIM-1 TIMP-1 CTGF KIM-1 TFF-3 CTGF KIM-1 VEGF CTGF microalbumin NGAL CTGF microalbumin osteopontin CTGF microalbumin THP CTGF microalbumin TIMP-1 CTGF microalbumin TFF-3 CTGF microalbumin VEGF CTGF NGAL osteopontin CTGF NGAL THP CTGF NGAL TIMP-1 CTGF NGAL TFF-3 CTGF NGAL VEGF CTGF osteopontin THP CTGF osteopontin TIMP-1 CTGF osteopontin TFF-3 CTGF osteopontin VEGF CTGF THP TIMP-1 CTGF THP TFF-3 CTGF THP VEGF CTGF TIMP-1 TFF-3 CTGF TIMP-1 VEGF CTGF TFF-3 VEGF creatinine cystatin C GST-alpha creatinine cystatin C KIM-1 creatinine cystatin C microalbumin creatinine cystatin C NGAL creatinine cystatin C osteopontin creatinine cystatin C THP creatinine cystatin C TIMP-1 creatinine cystatin C TFF-3 creatinine cystatin C VEGF creatinine GST-alpha KIM-1 creatinine GST-alpha microalbumin creatinine GST-alpha NGAL creatinine GST-alpha osteopontin creatinine GST-alpha THP creatinine GST-alpha TIMP-1 creatinine GST-alpha TFF-3 creatinine GST-alpha VEGF creatinine KIM-1 microalbumin creatinine KIM-1 NGAL creatinine KIM-1 osteopontin creatinine KIM-1 THP creatinine KIM-1 TIMP-1 creatinine KIM-1 TFF-3 creatinine KIM-1 VEGF creatinine microalbumin NGAL creatinine microalbumin osteopontin creatinine microalbumin THP creatinine microalbumin TIMP-1 creatinine microalbumin TFF-3 creatinine microalbumin VEGF creatinine NGAL osteopontin creatinine NGAL THP creatinine NGAL TIMP-1 creatinine NGAL TFF-3 creatinine NGAL VEGF creatinine osteopontin THP creatinine osteopontin TIMP-1 creatinine osteopontin TFF-3 creatinine osteopontin VEGF creatinine THP TIMP-1 creatinine THP TFF-3 creatinine THP VEGF creatinine TIMP-1 TFF-3 creatinine TIMP-1 VEGF creatinine TFF-3 VEGF cystatin C GST-alpha KIM-1 cystatin C GST-alpha microalbumin cystatin C GST-alpha NGAL cystatin C GST-alpha osteopontin cystatin C GST-alpha THP cystatin C GST-alpha TIMP-1 cystatin C GST-alpha TFF-3 cystatin C GST-alpha VEGF cystatin C KIM-1 microalbumin cystatin C KIM-1 NGAL cystatin C KIM-1 osteopontin cystatin C KIM-1 THP cystatin C KIM-1 TIMP-1 cystatin C KIM-1 TFF-3 cystatin C KIM-1 VEGF cystatin C microalbumin NGAL cystatin C microalbumin osteopontin cystatin C microalbumin THP cystatin C microalbumin TIMP-1 cystatin C microalbumin TFF-3 cystatin C microalbumin VEGF cystatin C NGAL osteopontin cystatin C NGAL THP cystatin C NGAL TIMP-1 cystatin C NGAL TFF-3 cystatin C NGAL VEGF cystatin C osteopontin THP cystatin C osteopontin TIMP-1 cystatin C osteopontin TFF-3 cystatin C osteopontin VEGF cystatin C THP TIMP-1 cystatin C THP TFF-3 cystatin C THP VEGF cystatin C TIMP-1 TFF-3 cystatin C TIMP-1 VEGF cystatin C TFF-3 VEGF GST-alpha KIM-1 microalbumin GST-alpha KIM-1 NGAL GST-alpha KIM-1 osteopontin GST-alpha KIM-1 THP GST-alpha KIM-1 TIMP-1 GST-alpha KIM-1 TFF-3 GST-alpha KIM-1 VEGF GST-alpha microalbumin NGAL GST-alpha microalbumin osteopontin GST-alpha microalbumin THP GST-alpha microalbumin TIMP-1 GST-alpha microalbumin TFF-3 GST-alpha microalbumin VEGF GST-alpha NGAL osteopontin GST-alpha NGAL THP GST-alpha NGAL TIMP-1 GST-alpha NGAL TFF-3 GST-alpha NGAL VEGF GST-alpha osteopontin THP GST-alpha osteopontin TIMP-1 GST-alpha osteopontin TFF-3 GST-alpha osteopontin VEGF GST-alpha THP TIMP-1 GST-alpha THP TFF-3 GST-alpha THP VEGF GST-alpha TIMP-1 TFF-3 GST-alpha TIMP-1 VEGF GST-alpha TFF-3 VEGF KIM-1 microalbumin NGAL KIM-1 microalbumin osteopontin KIM-1 microalbumin THP KIM-1 microalbumin TIMP-1 KIM-1 microalbumin TFF-3 KIM-1 microalbumin VEGF KIM-1 NGAL osteopontin KIM-1 NGAL THP KIM-1 NGAL TIMP-1 KIM-1 NGAL TFF-3 KIM-1 NGAL VEGF KIM-1 osteopontin THP KIM-1 osteopontin TIMP-1 KIM-1 osteopontin TFF-3 KIM-1 osteopontin VEGF KIM-1 THP TIMP-1 KIM-1 THP TFF-3 KIM-1 THP VEGF KIM-1 TIMP-1 TFF-3 KIM-1 TIMP-1 VEGF KIM-1 TFF-3 VEGF microalbumin NGAL osteopontin microalbumin NGAL THP microalbumin NGAL TIMP-1 microalbumin NGAL TFF-3 microalbumin NGAL VEGF microalbumin osteopontin THP microalbumin osteopontin TIMP-1 microalbumin osteopontin TFF-3 microalbumin osteopontin VEGF microalbumin THP TIMP-1 microalbumin THP TFF-3 microalbumin THP VEGF microalbumin TIMP-1 TFF-3 microalbumin TIMP-1 VEGF microalbumin TFF-3 VEGF NGAL osteopontin THP NGAL osteopontin TIMP-1 NGAL osteopontin TFF-3 NGAL osteopontin VEGF NGAL THP TIMP-1 NGAL THP TFF-3 NGAL THP VEGF NGAL TIMP-1 TFF-3 NGAL TIMP-1 VEGF NGAL TFF-3 VEGF osteopontin THP TIMP-1 osteopontin THP TFF-3 osteopontin THP VEGF osteopontin TIMP-1 TFF-3 osteopontin TIMP-1 VEGF osteopontin TFF-3 VEGF THP TIMP-1 TFF-3 THP TIMP-1 VEGF THP TFF-3 VEGF TIMP-1 TFF-3 VEGF

In one exemplary embodiment, the combination of sample analytes may include creatinine, KIM-1, and THP. In another exemplary embodiment, the combination of sample analytes may include microalbumin, creatinine, and KIM-1. In yet another exemplary embodiment, the combination of sample analytes may include KIM-1, THP, and B2M. In still another exemplary embodiment, the combination of sample analytes may include microalbumin, A1M, and creatinine. In an alternative exemplary embodiment, the sample is a urine sample, and the combination of sample analytes may include microalbumin, alpha-1 microglobulin, NGAL, KIM-1, THP, and clusterin. In another alternative exemplary embodiment, the sample is a plasma sample, and the combination of sample analytes may include alpha-1 microglobulin, cystatin C, THP, beta-2 microglobulin, TIMP-1, and KIM-1.

III. Test Sample

The method for diagnosing, monitoring, or determining a renal disorder involves determining the presence of sample analytes in a test sample. A test sample, as defined herein, is an amount of bodily fluid taken from a mammal. Non-limiting examples of bodily fluids include urine, blood, plasma, serum, saliva, semen, perspiration, tears, mucus, and tissue lysates. In an exemplary embodiment, the bodily fluid contained in the test sample is urine, plasma, or serum.

(a) Mammals

A mammal, as defined herein, is any organism that is a member of the class Mammalia. Non-limiting examples of mammals appropriate for the various embodiments may include humans, apes, monkeys, rats, mice, dogs, cats, pigs, and livestock including cattle and oxen. In an exemplary embodiment, the mammal is a human.

(b) Devices and Methods of Taking Bodily Fluids from Mammals

The bodily fluids of the test sample may be taken from the mammal using any known device or method so long as the analytes to be measured by the multiplexed assay are not rendered undetectable by the multiplexed assay. Non-limiting examples of devices or methods suitable for taking bodily fluid from a mammal include urine sample cups, urethral catheters, swabs, hypodermic needles, thin needle biopsies, hollow needle biopsies, punch biopsies, metabolic cages, and aspiration.

In order to adjust the expected concentrations of the sample analytes in the test sample to fall within the dynamic range of the multiplexed assay, the test sample may be diluted to reduce the concentration of the sample analytes prior to analysis. The degree of dilution may depend on a variety of factors including but not limited to the type of multiplexed assay used to measure the analytes, the reagents utilized in the multiplexed assay, and the type of bodily fluid contained in the test sample. In one embodiment, the test sample is diluted by adding a volume of diluent ranging from about ½ of the original test sample volume to about 50,000 times the original test sample volume.

In one exemplary embodiment, if the test sample is human urine and the multiplexed assay is an antibody-based capture-sandwich assay, the test sample is diluted by adding a volume of diluent that is about 100 times the original test sample volume prior to analysis. In another exemplary embodiment, if the test sample is human serum and the multiplexed assay is an antibody-based capture-sandwich assay, the test sample is diluted by adding a volume of diluent that is about 5 times the original test sample volume prior to analysis. In yet another exemplary embodiment, if the test sample is human plasma and the multiplexed assay is an antibody-based capture-sandwich assay, the test sample is diluted by adding a volume of diluent that is about 2,000 times the original test sample volume prior to analysis.

The diluent may be any fluid that does not interfere with the function of the multiplexed assay used to measure the concentration of the analytes in the test sample. Non-limiting examples of suitable diluents include deionized water, distilled water, saline solution, Ringer's solution, phosphate buffered saline solution, TRIS-buffered saline solution, standard saline citrate, and HEPES-buffered saline.

IV. Multiplexed Assay Device

In one embodiment, the concentration of a combination of sample analytes is measured using a multiplexed assay device capable of measuring the concentrations of up to sixteen of the biomarker analytes. A multiplexed assay device, as defined herein, is an assay capable of simultaneously determining the concentration of three or more different sample analytes using a single device and/or method. Any known method of measuring the concentration of the biomarker analytes may be used for the multiplexed assay device. Non-limiting examples of measurement methods suitable for the multiplexed assay device may include electrophoresis, mass spectrometry, protein microarrays, surface plasmon resonance and immunoassays including but not limited to western blot, immunohistochemical staining, enzyme-linked immunosorbent assay (ELISA) methods, and particle-based capture-sandwich immunoassays.

(a) Multiplexed Immunoassay Device

In one embodiment, the concentrations of the analytes in the test sample are measured using a multiplexed immunoassay device that utilizes capture antibodies marked with indicators to determine the concentration of the sample analytes.

(i) Capture Antibodies

In the same embodiment, the multiplexed immunoassay device includes three or more capture antibodies. Capture antibodies, as defined herein, are antibodies in which the antigenic determinant is one of the biomarker analytes. Each of the at least three capture antibodies has a unique antigenic determinant that is one of the biomarker analytes. When contacted with the test sample, the capture antibodies form antigen-antibody complexes in which the analytes serve as antigens.

The term “antibody,” as used herein, encompasses a monoclonal ab, an antibody fragment, a chimeric antibody, and a single-chain antibody.

In some embodiments, the capture antibodies may be attached to a substrate in order to immobilize any analytes captured by the capture antibodies. Non-limiting examples of suitable substrates include paper, cellulose, glass, or plastic strips, beads, or surfaces, such as the inner surface of the well of a microtitration tray. Suitable beads may include polystyrene or latex microspheres.

(ii) Indicators

In one embodiment of the multiplexed immunoassay device, an indicator is attached to each of the three or more capture antibodies. The indicator, as defined herein, is any compound that registers a measurable change to indicate the presence of one of the sample analytes when bound to one of the capture antibodies. Non-limiting examples of indicators include visual indicators and electrochemical indicators.

Visual indicators, as defined herein, are compounds that register a change by reflecting a limited subset of the wavelengths of light illuminating the indicator, by fluorescing light after being illuminated, or by emitting light via chemiluminescence. The change registered by visual indicators may be in the visible light spectrum, in the infrared spectrum, or in the ultraviolet spectrum. Non-limiting examples of visual indicators suitable for the multiplexed immunoassay device include nanoparticulate gold, organic particles such as polyurethane or latex microspheres loaded with dye compounds, carbon black, fluorophores, phycoerythrin, radioactive isotopes, nanoparticles, quantum dots, and enzymes such as horseradish peroxidase or alkaline phosphatase that react with a chemical substrate to form a colored or chemiluminescent product.

Electrochemical indicators, as defined herein, are compounds that register a change by altering an electrical property. The changes registered by electrochemical indicators may be an alteration in conductivity, resistance, capacitance, current conducted in response to an applied voltage, or voltage required to achieve a desired current. Non-limiting examples of electrochemical indicators include redox species such as ascorbate (vitamin C), vitamin E, glutathione, polyphenols, catechols, quercetin, phytoestrogens, penicillin, carbazole, murranes, phenols, carbonyls, benzoates, and trace metal ions such as nickel, copper, cadmium, iron and mercury.

In this same embodiment, the test sample containing a combination of three or more sample analytes is contacted with the capture antibodies and allowed to form antigen-antibody complexes in which the sample analytes serve as the antigens. After removing any uncomplexed capture antibodies, the concentrations of the three or more analytes are determined by measuring the change registered by the indicators attached to the capture antibodies.

In one exemplary embodiment, the indicators are polyurethane or latex microspheres loaded with dye compounds and phycoerythrin.

(b) Multiplexed Sandwich Immunoassay Device

In another embodiment, the multiplexed immunoassay device has a sandwich assay format. In this embodiment, the multiplexed sandwich immunoassay device includes three or more capture antibodies as previously described. However, in this embodiment, each of the capture antibodies is attached to a capture agent that includes an antigenic moiety. The antigenic moiety serves as the antigenic determinant of a detection antibody, also included in the multiplexed immunoassay device of this embodiment. In addition, an indicator is attached to the detection antibody.

In this same embodiment, the test sample is contacted with the capture antibodies and allowed to form antigen-antibody complexes in which the sample analytes serve as antigens. The detection antibodies are then contacted with the test sample and allowed to form antigen-antibody complexes in which the capture agent serves as the antigen for the detection antibody. After removing any uncomplexed detection antibodies the concentration of the analytes are determined by measuring the changes registered by the indicators attached to the detection antibodies.

(c) Multiplexing Approaches

In the various embodiments of the multiplexed immunoassay devices, the concentrations of each of the sample analytes may be determined using any approach known in the art. In one embodiment, a single indicator compound is attached to each of the three or more antibodies. In addition, each of the capture antibodies having one of the sample analytes as an antigenic determinant is physically separated into a distinct region so that the concentration of each of the sample analytes may be determined by measuring the changes registered by the indicators in each physically separate region corresponding to each of the sample analytes.

In another embodiment, each antibody having one of the sample analytes as an antigenic determinant is marked with a unique indicator. In this manner, a unique indicator is attached to each antibody having a single sample analyte as its antigenic determinant. In this embodiment, all antibodies may occupy the same physical space. The concentration of each sample analyte is determined by measuring the change registered by the unique indicator attached to the antibody having the sample analyte as an antigenic determinant.

(d) Microsphere-Based Capture-Sandwich Immunoassay Device

In an exemplary embodiment, the multiplexed immunoassay device is a microsphere-based capture-sandwich immunoassay device. In this embodiment, the device includes a mixture of three or more capture-antibody microspheres, in which each capture-antibody microsphere corresponds to one of the biomarker analytes. Each capture-antibody microsphere includes a plurality of capture antibodies attached to the outer surface of the microsphere. In this same embodiment, the antigenic determinant of all of the capture antibodies attached to one microsphere is the same biomarker analyte.

In this embodiment of the device, the microsphere is a small polystyrene or latex sphere that is loaded with an indicator that is a dye compound. The microsphere may be between about 3 μm and about 5 μm in diameter. Each capture-antibody microsphere corresponding to one of the biomarker analytes is loaded with the same indicator. In this manner, each capture-antibody microsphere corresponding to a biomarker analyte is uniquely color-coded.

In this same exemplary embodiment, the multiplexed immunoassay device further includes three or more biotinylated detection antibodies in which the antigenic determinant of each biotinylated detection antibody is one of the biomarker analytes. The device further includes a plurality of streptaviden proteins complexed with a reporter compound. A reporter compound, as defined herein, is an indicator selected to register a change that is distinguishable from the indicators used to mark the capture-antibody microspheres.

The concentrations of the sample analytes may be determined by contacting the test sample with a mixture of capture-antigen microspheres corresponding to each sample analyte to be measured. The sample analytes are allowed to form antigen-antibody complexes in which a sample analyte serves as an antigen and a capture antibody attached to the microsphere serves as an antibody. In this manner, the sample analytes are immobilized onto the capture-antigen microspheres. The biotinylated detection antibodies are then added to the test sample and allowed to form antigen-antibody complexes in which the analyte serves as the antigen and the biotinylated detection antibody serves as the antibody. The streptaviden-reporter complex is then added to the test sample and allowed to bind to the biotin moieties of the biotinylated detection antibodies. The antigen-capture microspheres may then be rinsed and filtered.

In this embodiment, the concentration of each analyte is determined by first measuring the change registered by the indicator compound embedded in the capture-antigen microsphere in order to identify the particular analyte. For each microsphere corresponding to one of the biomarker analytes, the quantity of analyte immobilized on the microsphere is determined by measuring the change registered by the reporter compound attached to the microsphere.

For example, the indicator embedded in the microspheres associated with one sample analyte may register an emission of orange light, and the reporter may register an emission of green light. In this example, a detector device may measure the intensity of orange light and green light separately. The measured intensity of the green light would determine the concentration of the analyte captured on the microsphere, and the intensity of the orange light would determine the specific analyte captured on the microsphere.

Any sensor device may be used to detect the changes registered by the indicators embedded in the microspheres and the changes registered by the reporter compound, so long as the sensor device is sufficiently sensitive to the changes registered by both indicator and reporter compound. Non-limiting examples of suitable sensor devices include spectrophotometers, photosensors, colorimeters, cyclic coulometry devices, and flow cytometers. In an exemplary embodiment, the sensor device is a flow cytometer.

V. Method for Diagnosing, Monitoring, or Determining a Renal Disorder

In one embodiment, a method is provided for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder that includes providing a test sample, determining the concentration of a combination of three or more sample analytes, comparing the measured concentrations of the combination of sample analytes to the entries of a dataset, and identifying diabetic nephropathy or an associated disorder based on the comparison between the concentrations of the sample analytes and the minimum diagnostic concentrations contained within each entry of the dataset.

(a) Diagnostic Dataset

In an embodiment, the concentrations of the sample analytes are compared to the entries of a dataset. In this embodiment, each entry of the dataset includes a combination of three or more minimum diagnostic concentrations indicative of a particular renal disorder. A minimum diagnostic concentration, as defined herein, is the concentration of an analyte that defines the limit between the concentration range corresponding to normal, healthy renal function and the concentration reflective of a particular renal disorder. In one embodiment, each minimum diagnostic concentration is the maximum concentration of the range of analyte concentrations for a healthy, normal individual. The minimum diagnostic concentration of an analyte depends on a number of factors including but not limited to the particular analyte and the type of bodily fluid contained in the test sample. As an illustrative example, Table 1 lists the expected normal ranges of the biomarker analytes in human plasma, serum, and urine.

TABLE 1 Normal Concentration Ranges In Human Plasma, Serum, and Urine Samples Plasma Sera Urine Analyte Units low high low high low high Calbindin ng/ml — <5.0 — <2.6 4.2 233 Clusterin μg/ml 86 134 37 152 — <0.089 CTGF ng/ml 2.8 7.5 — <8.2 — <0.90 GST-alpha ng/ml 6.7 62 1.2 52 — <26 KIM-1 ng/ml 0.053 0.57 — <0.35 0.023 0.67 VEGF pg/ml 222 855 219 1630 69 517 B2M μg/ml 0.68 2.2 1.00 2.6 <0.17 Cyst C ng/ml 608 1170 476 1250 3.9 79 NGAL ng/ml 89 375 102 822 2.9 81 OPN ng/ml 4.1 25 0.49 12 291 6130 TIMP-1 ng/ml 50 131 100 246 — <3.9 A1M μg/ml 6.2 16 5.7 17 — <4.2 THP μg/ml 0.0084 0.052 0.0079 0.053 0.39 2.6 TFF3 μg/ml 0.040 0.49 0.021 0.17 — <21 Creatinine mg/dL — — — — 13 212 Microalbumin μg/ml — — — — — >16

In one embodiment, the high values shown for each of the biomarker analytes in Table 1 for the analytic concentrations in human plasma, sera and urine are the minimum diagnostics values for the analytes in human plasma, sera, and urine, respectively. In one exemplary embodiment, the minimum diagnostic concentration in human plasma of alpha-1 microglobulin is about 16 μg/ml, beta-2 microglobulin is about 2.2 μg/ml, calbindin is greater than about 5 ng/ml, clusterin is about 134 μg/ml, CTGF is about 16 ng/ml, cystatin C is about 1170 ng/ml, GST-alpha is about 62 ng/ml, KIM-1 is about 0.57 ng/ml, NGAL is about 375 ng/ml, osteopontin is about 25 ng/ml, THP is about 0.052 μg/ml, TIMP-1 is about 131 ng/ml, TFF-3 is about 0.49 μg/ml, and VEGF is about 855 μg/ml.

In another exemplary embodiment, the minimum diagnostic concentration in human sera of alpha-1 microglobulin is about 17 μg/ml, beta-2 microglobulin is about 2.6 μg/ml, calbindin is greater than about 2.6 ng/ml, clusterin is about 152 μg/ml, CTGF is greater than about 8.2 ng/ml, cystatin C is about 1250 ng/ml, GST-alpha is about 52 ng/ml, KIM-1 is greater than about 0.35 ng/ml, NGAL is about 822 ng/ml, osteopontin is about 12 ng/ml, THP is about 0.053 μg/ml, TIMP-1 is about 246 ng/ml, TFF-3 is about 0.17 μg/ml, and VEGF is about 1630 μg/ml.

In yet another exemplary embodiment, the minimum diagnostic concentration in human urine of alpha-1 microglobulin is about 233 μg/ml, beta-2 microglobulin is greater than about 0.17 μg/ml, calbindin is about 233 ng/ml, clusterin is greater than about 0.089 μg/ml, CTGF is greater than about 0.90 ng/ml, cystatin C is about 1170 ng/ml, GST-alpha is greater than about 26 ng/ml, KIM-1 is about 0.67 ng/ml, NGAL is about 81 ng/ml, osteopontin is about 6130 ng/ml, THP is about 2.6 μg/ml, TIMP-1 is greater than about 3.9 ng/ml, TFF-3 is greater than about 21 μg/ml, and VEGF is about 517 μg/ml.

In one embodiment, the minimum diagnostic concentrations represent the maximum level of analyte concentrations falling within an expected normal range. Diabetic nephropathy or an associated disorder may be indicated if the concentration of an analyte is higher than the minimum diagnostic concentration for the analyte.

If diminished concentrations of a particular analyte are known to be associated with diabetic nephropathy or an associated disorder, the minimum diagnostic concentration may not be an appropriate diagnostic criterion for identifying diabetic nephropathy or an associated disorder indicated by the sample analyte concentrations. In these cases, a maximum diagnostic concentration may define the limit between the expected normal concentration range for the analyte and a sample concentration reflective of diabetic nephropathy or an associated disorder. In those cases in which a maximum diagnostic concentration is the appropriate diagnostic criterion, sample concentrations that fall below a maximum diagnostic concentration may indicate diabetic nephropathy or an associated disorder.

A critical feature of the method of the multiplexed analyte panel is that a combination of sample analyte concentrations may be used to diagnose diabetic nephropathy or an associated disorder. In addition to comparing subsets of the biomarker analyte concentrations to diagnostic criteria, the analytes may be algebraically combined and compared to corresponding diagnostic criteria. In one embodiment, two or more sample analyte concentrations may be added and/or subtracted to determine a combined analyte concentration. In another embodiment, two or more sample analyte concentrations may be multiplied and/or divided to determine a combined analyte concentration. To identify diabetic nephropathy or an associated disorder, the combined analyte concentration may be compared to a diagnostic criterion in which the corresponding minimum or maximum diagnostic concentrations are combined using the same algebraic operations used to determine the combined analyte concentration.

In yet another embodiment, the analyte concentration measured from a test sample containing one type of body fluid may be algebraically combined with an analyte concentration measured from a second test sample containing a second type of body fluid to determine a combined analyte concentration. For example, the ratio of urine calbindin to plasma calbindin may be determined and compared to a corresponding minimum diagnostic urine:plasma calbindin ratio to identify a particular renal disorder.

A variety of methods known in the art may be used to define the diagnostic criteria used to identify diabetic nephropathy or an associated disorder. In one embodiment, any sample concentration falling outside the expected normal range indicates diabetic nephropathy or an associated disorder. In another embodiment, the multiplexed analyte panel may be used to evaluate the analyte concentrations in test samples taken from a population of patients having diabetic nephropathy or an associated disorder and compared to the normal expected analyte concentration ranges. In this same embodiment, any sample analyte concentrations that are significantly higher or lower than the expected normal concentration range may be used to define a minimum or maximum diagnostic concentration, respectively. A number of studies comparing the biomarker concentration ranges of a population of patients having a renal disorder to the corresponding analyte concentrations from a population of normal healthy subjects are described in the examples section below.

In an exemplary embodiment, an analyte value in a test sample higher than the minimum diagnostic value for the top 3 analytes of the particular sample type (e.g. plasma, urine, etc.), wherein the top 3 are determined by the random forest classification method may result in a diagnosis of diabetic nephropathy.

VI. Automated Method for Diagnosing, Monitoring, or Determining a Renal Disorder

In one embodiment, a system for diagnosing, monitoring, or determining diabetic nephropathy or an associated disorder in a mammal is provided that includes a database to store a plurality of renal disorder database entries, and a processing device that includes the modules of a renal disorder determining application. In this embodiment, the modules are executable by the processing device, and include an analyte input module, a comparison module, and an analysis module.

The analyte input module receives three or more sample analyte concentrations that include the biomarker analytes. In one embodiment, the sample analyte concentrations are entered as input by a user of the application. In another embodiment, the sample analyte concentrations are transmitted directly to the analyte input module by the sensor device used to measure the sample analyte concentration via a data cable, infrared signal, wireless connection or other methods of data transmission known in the art.

The comparison module compares each sample analyte concentration to an entry of a renal disorder database. Each entry of the renal disorder database includes a list of minimum diagnostic concentrations reflective of a particular renal disorder. The entries of the renal disorder database may further contain additional minimum diagnostic concentrations to further define diagnostic criteria including but not limited to minimum diagnostic concentrations for additional types of bodily fluids, additional types of mammals, and severities of a particular disorder.

The analysis module determines a most likely renal disorder by combining the particular renal disorders identified by the comparison module for all of the sample analyte concentrations. In one embodiment, the most likely renal disorder is the particular renal disorder from the database entry having the most minimum diagnostic concentrations that are less than the corresponding sample analyte concentrations. In another embodiment, the most likely renal disorder is the particular renal disorder from the database entry having minimum diagnostic concentrations that are all less than the corresponding sample analyte concentrations. In yet other embodiments, the analysis module combines the sample analyte concentrations algebraically to calculate a combined sample analyte concentration that is compared to a combined minimum diagnostic concentration calculated from the corresponding minimum diagnostic criteria using the same algebraic operations. Other combinations of sample analyte concentrations from within the same test sample, or combinations of sample analyte concentrations from two or more different test samples containing two or more different bodily fluids may be used to determine a particular renal disorder in still other embodiments.

The system includes one or more processors and volatile and/or nonvolatile memory and can be embodied by or in one or more distributed or integrated components or systems. The system may include computer readable media (CRM) on which one or more algorithms, software, modules, data, and/or firmware is loaded and/or operates and/or which operates on the one or more processors to implement the systems and methods identified herein. The computer readable media may include volatile media, nonvolatile media, removable media, non-removable media, and/or other media or mediums that can be accessed by a general purpose or special purpose computing device. For example, computer readable media may include computer storage media and communication media, including but not limited to computer readable media. Computer storage media further may include volatile, nonvolatile, removable, and/or non-removable media implemented in a method or technology for storage of information, such as computer readable instructions, data structures, program modules, and/or other data. Communication media may, for example, embody computer readable instructions, data structures, program modules, algorithms, and/or other data, including but not limited to as or in a modulated data signal. The communication media may be embodied in a carrier wave or other transport mechanism and may include an information delivery method. The communication media may include wired and wireless connections and technologies and may be used to transmit and/or receive wired or wireless communications. Combinations and/or sub-combinations of the above and systems, components, modules, and methods and processes described herein may be made.

The following examples are included to demonstrate preferred embodiments of the invention.

EXAMPLES

The following examples illustrate various iterations of the invention.

Example 1: Least Detectable Dose and Lower Limit of Quantitation of Assay for Analytes Associated with Renal Disorders

To assess the least detectable doses (LDD) and lower limits of quantitation (LLOQ) of a variety of analytes associated with renal disorders, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.

The concentrations of the analytes were measured using a capture-sandwich assay using antigen-specific antibodies. For each analyte, a range of standard sample dilutions ranging over about four orders of magnitude of analyte concentration were measured using the assay in order to obtain data used to construct a standard dose response curve. The dynamic range for each of the analytes, defined herein as the range of analyte concentrations measured to determine its dose response curve, is presented below.

To perform the assay, 5 μL of a diluted mixture of capture-antibody microspheres were mixed with 5 μL of blocker and 10 μL of pre-diluted standard sample in each of the wells of a hard-bottom microtiter plate. After incubating the hard-bottom plate for 1 hour, 10 μL of biotinylated detection antibody was added to each well, and then the hard-bottom plate was incubated for an additional hour. 10 μL of diluted streptavidin-phycoerythrin was added to each well and then the hard-bottom plate was incubated for another 60 minutes.

A filter-membrane microtiter plate was pre-wetted by adding 100 μL wash buffer, and then aspirated using a vacuum manifold device. The contents of the wells of the hard-bottom plate were then transferred to the corresponding wells of the filter-membrane plate. All wells of the hard-bottom plate were vacuum-aspirated and the contents were washed twice with 100 μL of wash buffer. After the second wash, 100 μL of wash buffer was added to each well, and then the washed microspheres were resuspended with thorough mixing. The plate was then analyzed using a Luminex 100 Analyzer (Luminex Corporation, Austin, Tex., USA). Dose response curves were constructed for each analyte by curve-fitting the median fluorescence intensity (MFI) measured from the assays of diluted standard samples containing a range of analyte concentrations.

The least detectable dose (LDD) was determined by adding three standard deviations to the average of the MFI signal measured for 20 replicate samples of blank standard solution (i.e. standard solution containing no analyte). The MFI signal was converted to an LDD concentration using the dose response curve and multiplied by a dilution factor of 2.

The lower limit of quantification (LLOQ), defined herein as the point at which the coefficient of variation (CV) for the analyte measured in the standard samples was 30%, was determined by the analysis of the measurements of increasingly diluted standard samples. For each analyte, the standard solution was diluted by 2 fold for 8 dilutions. At each stage of dilution, samples were assayed in triplicate, and the CV of the analyte concentration at each dilution was calculated and plotted as a function of analyte concentration. The LLOQ was interpolated from this plot and multiplied by a dilution factor of 2.

The LDD and LLOQ results for each analyte are summarized in Table 2:

TABLE 2 LDD, LLOQ, and Dynamic Range of Analyte Assay Dynamic Range Analyte Units LDD LLOQ minimum maximum Calbindin ng/mL 1.1 3.1 0.516 2580 Clusterin ng/mL 2.4 2.3 0.676 3378 CTGF ng/mL 1.3 3.8 0.0794 400 GST-alpha ng/mL 1.4 3.6 0.24 1,200 KIM-1 ng/mL 0.016 0.028 0.00478 24 VEGF pg/mL 4.4 20 8.76 44,000 β-2M μg/mL 0.012 0.018 0.0030 15 Cystatin C ng/mL 2.8 3.7 0.60 3,000 NGAL ng/mL 4.1 7.8 1.2 6,000 Osteopontin ng/mL 29 52 3.9 19,500 TIMP-1 ng/mL 0.71 1.1 0.073 365 A-1M μg/mL 0.059 0.29 0.042 210 THP μg/mL 0.46 0.30 0.16 800 TFF-3 μg/mL 0.06 0.097 0.060 300

The results of this experiment characterized the least detectible dose and the lower limit of quantification for fourteen analytes associated with various renal disorders using a capture-sandwich assay.

Example 2: Precision of Assay for Analytes Associated with Renal Disorders

To assess the precision of an assay used to measure the concentration of analytes associated with renal disorders, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For each analyte, three concentration levels of standard solution were measured in triplicate during three runs using the methods described in Example 1. The percent errors for each run at each concentration are presented in Table 3 for all of the analytes tested:

TABLE 3 Precision of Analyte Assay Average concentration Run 1 Run 2 Run 2 Interrun Analyte (ng/mL) Error (%) Error (%) Error (%) Error (%) Calbindin 4.0 6 2 6 13 36 5 3 2 7 281 1 6 0 3 Clusterin 4.4 4 9 2 6 39 5 1 6 8 229 1 3 0 2 CTGF 1.2 10 17 4 14 2.5 19 19 14 14 18 7 5 13 9 GST-alpha 3.9 14 7 5 10 16 13 7 10 11 42 1 16 6 8 KIM-1 0.035 2 0 5 13 0.32 4 5 2 8 2.9 0 5 7 4 VEGF 65 10 1 6 14 534 9 2 12 7 5,397 1 13 14 9 β-2M 0.040 6 1 8 5 0.43 2 2 0 10 6.7 6 5 11 6 Cystatin C 10.5 4 1 7 13 49 0 0 3 9 424 2 6 2 5 NGAL 18.1 11 3 6 13 147 0 0 6 5 1,070 5 1 2 5 Osteopontin 44 1 10 2 11 523 9 9 9 7 8,930 4 10 1 10 TIMP-1 2.2 13 6 3 13 26 1 1 4 14 130 1 3 1 4 A-1M 1.7 11 7 7 14 19 4 1 8 9 45 3 5 2 4 THP 9.4 3 10 11 11 15 3 7 8 6 37 4 5 0 5 TFF-3 0.3 13 3 11 12 4.2 5 8 5 7 1.2 3 7 0 13

The results of this experiment characterized the precision of a capture-sandwich assay for fourteen analytes associated with various renal disorders over a wide range of analyte concentrations. The precision of the assay varied between about 1% and about 15% error within a given run, and between about 5% and about 15% error between different runs. The percent errors summarized in Table 2 provide information concerning random error to be expected in an assay measurement caused by variations in technicians, measuring instruments, and times of measurement.

Example 3: Linearity of Assay for Analytes Associated with Renal Disorders

To assess the linearity of an assay used to measure the concentration of analytes associated with renal disorders, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For each analyte, three concentration levels of standard solution were measured in triplicate during three runs using the methods described in Example 1. Linearity of the assay used to measure each analyte was determined by measuring the concentrations of standard samples that were serially-diluted throughout the assay range. The % recovery was calculated as observed vs. expected concentration based on the dose-response curve. The results of the linearity analysis are summarized in Table 4.

TABLE 4 Linearity of Analyte Assay Expected Observed Recovery Analyte Dilution concentration concentration (%) Calbindin 1:2 61 61 100 (ng/mL) 1:4 30 32 106 1:8 15 17 110 Clusterin 1:2 41 41 100 (ng/mL) 1:4 21 24 116 1:8 10 11 111 CTGF 1:2 1.7 1.7 100 (ng/mL) 1:4 0.84 1.0 124 1:8 0.42 0.51 122 GST-alpha 1:2 25 25 100 (ng/mL) 1:4 12 14 115 1:8 6.2 8.0 129 KIM-1 1:2 0.87 0.87 100 (ng/mL) 1:4 0.41 0.41 101 1:8 0.21 0.19 93 VEGF 1:2 2,525 2,525 100 (pg/mL) 1:4 1,263 1,340 106 1:8 631 686 109 β-2M 1:100 0.63 0.63 100 (μg/mL) 1:200 0.31 0.34 106 1:400 0.16 0.17 107 Cystatin C 1:100 249 249 100 (ng/mL) 1:200 125 122 102 1:400 62 56 110 NGAL 1:100 1,435 1,435 100 (ng/mL) 1:200 718 775 108 1:400 359 369 103 Osteopontin 1:100 6,415 6,415 100 (ng/mL) 1:200 3,208 3,275 102 1:400 1,604 1,525 95 TIMP-1 1:100 35 35 100 (ng/mL) 1:200 18 18 100 1:400 8.8 8.8 100 A-1M 1:2000 37 37 100 (μg/mL) 1:4000 18 18 99 1:8000 9.1 9.2 99 THP 1:2000 28 28 100 (μg/mL) 1:4000 14 14 96 1:8000 6.7 7.1 94 TFF-3 1:2000 8.8 8.8 100 (μg/mL) 1:4000 3.8 4.4 86 1:8000 1.9 2.2 86

The results of this experiment demonstrated reasonably linear responses of the sandwich-capture assay to variations in the concentrations of the analytes in the tested samples.

Example 4: Spike Recovery of Analytes Associated with Renal Disorders

To assess the recovery of analytes spiked into urine, serum, and plasma samples by an assay used to measure the concentration of analytes associated with renal disorders, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For each analyte, three concentration levels of standard solution were spiked into known urine, serum, and plasma samples. Prior to analysis, all urine samples were diluted 1:2000 (sample: diluent), all plasma samples were diluted 1:5 (sample: diluent), and all serum samples were diluted 1:2000 (sample: diluent).

The concentrations of the analytes in the samples were measured using the methods described in Example 1. The average % recovery was calculated as the proportion of the measurement of analyte spiked into the urine, serum, or plasma sample (observed) to the measurement of analyte spiked into the standard solution (expected). The results of the spike recovery analysis are summarized in Table 5.

TABLE 5 Spike Recovery of Analyte Assay in Urine, Serum, and Plasma Samples Recovery Recovery Recovery in in in Spike Urine Serum Plasma Analyte Concentration Sample (%) Sample (%) Sample (%) Calbindin 66 76 82 83 (ng/mL) 35 91 77 71 18 80 82 73 average 82 80 76 Clusterin 80 72 73 75 (ng/mL) 37 70 66 72 20 90 73 70 average 77 70 72 CTGF 8.4 91 80 79 (ng/mL) 4.6 114 69 78 2.4 76 80 69 average 94 77 75 GST-alpha 27 75 84 80 (ng/mL) 15 90 75 81 7.1 82 84 72 average 83 81 78 KIM-1 0.63 87 80 83 (ng/mL) .029 119 74 80 0.14 117 80 78 average 107 78 80 VEGF 584 88 84 82 (pg/mL) 287 101 77 86 123 107 84 77 average 99 82 82 β-2M 0.97 117 98 98 (μg/mL) 0.50 124 119 119 0.24 104 107 107 average 115 108 105 Cystatin C 183 138 80 103 (ng/mL) 90 136 97 103 40 120 97 118 average 131 91 108 NGAL 426 120 105 111 (ng/mL) 213 124 114 112 103 90 99 113 average 111 106 112 Osteopontin 1,245 204 124 68 (ng/mL) 636 153 112 69 302 66 103 67 average 108 113 68 TIMP-1 25 98 97 113 (ng/mL) 12 114 89 103 5.7 94 99 113 average 102 95 110 A-1M 0.0028 100 101 79 (μg/mL) 0.0012 125 80 81 0.00060 118 101 82 Average 114 94 81 THP 0.0096 126 108 90 (μg/mL) 0.0047 131 93 91 0.0026 112 114 83 average 123 105 88 TFF-3 0.0038 105 114 97 (μg/mL) 0.0019 109 104 95 0.0010 102 118 93 average 105 112 95

The results of this experiment demonstrated that the sandwich-type assay is reasonably sensitive to the presence of all analytes measured, whether the analytes were measured in standard samples, urine samples, plasma samples, or serum samples.

Example 5: Matrix Interferences of Analytes Associated with Renal Disorders

To assess the matrix interference of hemoglobin, bilirubin, and triglycerides spiked into standard samples, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For each analyte, three concentration levels of standard solution were spiked into known urine, serum, and plasma samples. Matrix interference was assessed by spiking hemoglobin, bilirubin, and triglyceride into standard analyte samples and measuring analyte concentrations using the methods described in Example 1. A % recovery was determined by calculating the ratio of the analyte concentration measured from the spiked sample (observed) divided by the analyte concentration measured form the standard sample (expected). The results of the matrix interference analysis are summarized in Table 6.

TABLE 6 Matrix Interference of Hemoglobin, Bilirubin, and Triglyceride on the Measurement of Analytes Matrix Compound Maximum Overall Spiked into Spike Recovery Analyte Sample Concentration (%) Calbindin Hemoglobin 500 110 (mg/mL) Bilirubin 20 98 Triglyceride 500 117 Clusterin Hemoglobin 500 125 (mg/mL) Bilirubin 20 110 Triglyceride 500 85 CTGF Hemoglobin 500 91 (mg/mL) Bilirubin 20 88 Triglyceride 500 84 GST-alpha Hemoglobin 500 100 (mg/mL) Bilirubin 20 96 Triglyceride 500 96 KIM-1 Hemoglobin 500 108 (mg/mL) Bilirubin 20 117 Triglyceride 500 84 VEGF Hemoglobin 500 112 (mg/mL) Bilirubin 20 85 Triglyceride 500 114 β-2M Hemoglobin 500 84 (μg/mL) Bilirubin 20 75 Triglyceride 500 104 Cystatin C Hemoglobin 500 91 (ng/mL) Bilirubin 20 102 Triglyceride 500 124 NGAL Hemoglobin 500 99 (ng/mL) Bilirubin 20 92 Triglyceride 500 106 Osteopontin Hemoglobin 500 83 (ng/mL) Bilirubin 20 86 Triglyceride 500 106 TIMP-1 Hemoglobin 500 87 (ng/mL) Bilirubin 20 86 Triglyceride 500 93 A-1M Hemoglobin 500 103 (μg/mL) Bilirubin 20 110 Triglyceride 500 112 THP Hemoglobin 500 108 (μg/mL) Bilirubin 20 101 Triglyceride 500 121 TFF-3 Hemoglobin 500 101 (μg/mL) Bilirubin 20 101 Triglyceride 500 110

The results of this experiment demonstrated that hemoglobin, bilirubin, and triglycerides, three common compounds found in urine, plasma, and serum samples, did not significantly degrade the ability of the sandwich-capture assay to detect any of the analytes tested.

Example 6: Sample Stability of Analytes Associated with Renal Disorders

To assess the ability of analytes spiked into urine, serum, and plasma samples to tolerate freeze-thaw cycles, the following experiment was conducted. The analytes measured were alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. Each analyte was spiked into known urine, serum, and plasma samples at a known analyte concentration. The concentrations of the analytes in the samples were measured using the methods described in Example 1 after the initial addition of the analyte, and after one, two and three cycles of freezing and thawing. In addition, analyte concentrations in urine, serum and plasma samples were measured immediately after the addition of the analyte to the samples as well as after storage at room temperature for two hours and four hours, and after storage at 4° C. for 2 hours, four hours, and 24 hours.

The results of the freeze-thaw stability analysis are summarized in Table 7. The % recovery of each analyte was calculated as a percentage of the analyte measured in the sample prior to any freeze-thaw cycles.

TABLE 7 Freeze-Thaw Stability of the Analytes in Urine, Serum, and Plasma Period Urine Sample Serum Sample Plasma Sample and Concen- Recovery Concen- Recovery Concen- Recovery Analyte Temp tration (%) tration (%) tration (%) Calbindin Control 212 100 31 100 43 100 (ng/mL) 1x 221 104 30 96 41 94 2X 203 96 30 99 39 92 3X 234 110 30 97 40 93 Clusterin 0 315 100 232 100 187 100 (ng/mL) 1X 329 104 227 98 177 95 2X 341 108 240 103 175 94 3X 379 120 248 107 183 98 CTGF 0 6.7 100 1.5 100 1.2 100 (ng/mL) 1X 7.5 112 1.3 82 1.2 94 2X 6.8 101 1.4 90 1.2 100 3X 7.7 115 1.2 73 1.3 107 GST- 0 12 100 23 100 11 100 alpha 1X 13 104 24 105 11 101 (ng/mL) 2X 14 116 21 92 11 97 3X 14 111 23 100 12 108 KIM-1 0 1.7 100 0.24 100 0.24 100 (ng/mL) 1X 1.7 99 0.24 102 0.22 91 2X 1.7 99 0.22 94 0.19 78 3X 1.8 107 0.23 97 0.22 93 VEGF 0 1,530 100 1,245 100 674 100 (pg/mL) 1X 1,575 103 1,205 97 652 97 2X 1,570 103 1,140 92 612 91 3X 1,700 111 1,185 95 670 99 β-2M 0 0.0070 100 1.2 100 15 100 (μg/mL) 1X 0.0073 104 1.1 93 14 109 2X 0.0076 108 1.2 103 15 104 3X 0.0076 108 1.1 97 13 116 Cystatin 0 1,240 100 1,330 100 519 100 C 1X 1,280 103 1,470 111 584 113 (ng/mL) 2X 1,410 114 1,370 103 730 141 3X 1,420 115 1,380 104 589 113 NGAL 0 45 100 245 100 84 100 (ng/mL) 1x 46 102 179 114 94 112 2X 47 104 276 113 91 108 3X 47 104 278 113 91 109 Osteopontin 0 38 100 1.7 100 5.0 100 (ng/mL) 1X 42 110 1.8 102 5.5 110 2X 42 108 1.5 87 5.5 109 3X 42 110 1.3 77 5.4 107 TIMP-1 0 266 100 220 100 70 100 (ng/mL) 1x 265 100 220 10 75 108 2X 255 96 215 98 77 110 3X 295 111 228 104 76 109 A-1 M 0 14 100 26 100 4.5 100 (μg/mL) 1X 13 92 25 96 4.2 94 2X 15 107 25 96 4.3 97 3X 16 116 23 88 4.0 90 THP 0 4.6 100 31 100 9.2 100 (μg/mL) 1X 4.4 96 31 98 8.8 95 2X 5.0 110 31 100 9.2 100 3X 5.2 114 27 85 9.1 99 TFF-3 0 4.6 100 24 100 22 100 (μg/mL) 1X 4.4 96 23 98 22 103 2X 5.0 110 24 103 22 101 3X 5.2 114 19 82 22 102

The results of the short-term stability assessment are summarized in Table 8. The % recovery of each analyte was calculated as a percentage of the analyte measured in the sample prior to any short-term storage.

TABLE 8 Short-Term Stability of Analytes in Urine, Serum, and Plasma Storage Urine Sample Serum Sample Plasma Sample Time/ Sample Recovery Sample Recovery Sample Recovery Analyte Temp Conc. (%) Conc. (%) Conc. (%) Calbindin Control 226 100 33 100 7 100 (ng/mL) 2 hr/ 242 107 30 90 6.3 90 room temp 2 hr. @ 228 101 29 89 6.5 93 4° C. 4 hr @ 240 106 28 84 5.6 79 room temp 4 hr @ 202 89 29 86 5.5 79 4° C. 24 hr. @ 199 88 26 78 7.1 101 4° C. Clusterin Control 185 100 224 100 171 100 (ng/mL) 2 hr @ 173 94 237 106 180 105 room temp 2 hr. @ 146 79 225 100 171 100 4° C. 4 hr @ 166 89 214 96 160 94 room temp 4 hr @ 157 85 198 88 143 84 4° C. 24 hr. @ 185 100 207 92 162 94 4° C. CTGF Control 1.9 100 8.8 100 1.2 100 (ng/mL) 2 hr @ 1.9 99 6.7 76 1 83 room temp 2 hr. @ 1.8 96 8.1 92 1.1 89 4° C. 4 hr @ 2.1 113 5.6 64 1 84 room temp 4 hr. @ 1.7 91 6.4 74 0.9 78 4° C. 24 hr. @ 2.2 116 5.9 68 1.1 89 4° C. GST- Control 14 100 21 100 11 100 alpha 2 hr @ (ng/mL) room 11 75 23 107 11 103 temp 2 hr. @ 13 93 22 104 9.4 90 4° C. 4 hr @ 11 79 21 100 11 109 room temp 4 hr. @ 12 89 21 98 11 100 4° C. 24 hr. @ 13 90 22 103 14 129 4° C. KIM-1 Control 1.5 100 0.23 100 0.24 100 (ng/mL) 2 hr @ 1.2 78 0.2 86 0.22 90 room temp 2 hr. @ 1.6 106 0.23 98 0.21 85 4° C. 4 hr @ 1.3 84 0.19 82 0.2 81 room temp 4 hr. @ 1.4 90 0.22 93 0.19 80 4° C. 24 hr. @ 1.1 76 0.18 76 0.23 94 4° C. VEGF Control 851 100 1215 100 670 100 (pg/mL) 2 hr @ 793 93 1055 87 622 93 room temp 2 hr. @ 700 82 1065 88 629 94 4° C. 4 hr @ 704 83 1007 83 566 84 room temp 4 hr. @ 618 73 1135 93 544 81 4° C. 24 hr. @ 653 77 1130 93 589 88 4° C. β-2M Control 0.064 100 2.6 100 1.2 100 (μg/mL) 2 hr @ 0.062 97 2.4 92 1.1 93 room temp 2 hr. @ 0.058 91 2.2 85 1.2 94 4° C. 4 hr @ 0.064 101 2.2 83 1.2 94 room temp 4 hr. @ 0.057 90 2.2 85 1.2 98 4° C. 24 hr. @ 0.06 94 2.5 97 1.3 103 4° C. Cystatin Control 52 100 819 100 476 100 C 2 hr @ 50 96 837 102 466 98 (ng/mL) room temp 2 hr. @ 44 84 884 108 547 115 4° C. 4 hr @ 49 93 829 101 498 105 room temp 4 hr. @ 46 88 883 108 513 108 4° C. 24 hr. @ 51 97 767 94 471 99 4° C. NGAL Control 857 100 302 100 93 100 (ng/mL) 2 hr @ 888 104 287 95 96 104 room temp 2 hr. @ 923 108 275 91 92 100 4° C. 4 hr @ 861 101 269 89 88 95 room temp 4 hr. @ 842 98 283 94 94 101 4° C. 24 hr. @ 960 112 245 81 88 95 4° C. Osteo- Control 2243 100 6.4 100 5.2 100 pontin 2 hr @ 2240 100 6.8 107 5.9 114 (ng/mL) room temp 2 hr. @ 2140 95 6.4 101 6.2 120 4° C. 4 hr @ 2227 99 6.9 108 5.8 111 room temp 4 hr. @ 2120 95 7.7 120 5.2 101 4° C. 24 hr. @ 2253 100 6.5 101 6 116 4° C. TIMP-1 Control 17 100 349 100 72 100 (ng/mL) 2 hr @ 17 98 311 89 70 98 room temp 2 hr. @ 16 94 311 89 68 95 4° C. 4 hr @ 17 97 306 88 68 95 room temp 4 hr. @ 16 93 329 94 74 103 4° C. 24 hr. @ 18 105 349 100 72 100 4° C. A-1 M Control 3.6 100 2.2 100 1 100 (μg/mL) 2 hr @ 3.5 95 2 92 1 105 room temp 2 hr. @ 3.4 92 2.1 97 0.99 99 4° C. 4 hr @ 3.2 88 2.2 101 0.99 96 room temp 4 hr. @ 3 82 2.2 99 0.97 98 4° C. 24 hr. @ 3 83 2.2 100 1 101 4° C. THP Control 1.2 100 34 100 2.1 100 (μg/mL) 2 hr @ 1.2 99 34 99 2 99 room temp 2 hr. @ 1.1 90 34 100 2 98 4° C. 4 hr @ 1.1 88 27 80 2 99 room temp 4 hr. @ 0.95 79 33 97 2 95 4° C. 24 hr. @ 0.91 76 33 98 2.4 116 4° C. TFF-3 Control 1230 100 188 100 2240 100 (μg/mL) 2 hr @ 1215 99 179 95 2200 98 room temp 2 hr. @ 1200 98 195 104 2263 101 4° C. 4 hr @ 1160 94 224 119 2097 94 room temp 4 hr. @ 1020 83 199 106 2317 103 4° C. 24 hr. @ 1030 84 229 122 1940 87 4° C.

The results of this experiment demonstrated that the analytes associated with renal disorders tested were suitably stable over several freeze/thaw cycles, and up to 24 hrs of storage at a temperature of 4° C.

Example 8: Analysis of Kidney Biomarkers in Plasma and Urine from Patients with Renal Injury

A screen for potential protein biomarkers in relation to kidney toxicity/damage was performed using a panel of biomarkers, in a set of urine and plasma samples from patients with documented renal damage. The investigated patient groups included diabetic nephropathy (DN), obstructive uropathy (OU), analgesic abuse (AA) and glomerulonephritis (GN) along with age, gender and BMI matched control groups. Multiplexed immunoassays were applied in order to quantify the following protein analytes: Alpha-1 Microglobulin (a1M), KIM-1, Microalbumin, Beta-2-Microglobulin (β2M), Calbindin, Clusterin, CystatinC, TreFoilFactor-3 (TFF-3), CTGF, GST-alpha, VEGF, Calbindin, Osteopontin, Tamm-HorsfallProtein (THP), TIMP-1 and NGAL.

Li-Heparin plasma and mid-stream spot urine samples were collected from four different patient groups. Samples were also collected from age, gender and BMI matched control subjects. 20 subjects were included in each group resulting in a total number of 160 urine and plasma samples. All samples were stored at −80° C. before use. Glomerular filtration rate for all samples was estimated using two different estimations (Modification of Diet in Renal Disease or MDRD, and the Chronic Kidney Disease Epidemiology Collaboration or CKD-EPI) to outline the eGFR (estimated glomerular filtration rate) distribution within each patient group (FIG. 1). Protein analytes were quantified in human plasma and urine using multiplexed immunoassays in the Luminex xMAP™ platform. The microsphere-based multiplex immunoassays consist of antigen-specific antibodies and optimized reagents in a capture-sandwich format. Output data was given as g/ml calculated from internal standard curves. Because urine creatinine (uCr) correlates with renal filtration rate, data analysis was performed without correction for uCr. Univariate and multivariate data analysis was performed comparing all case vs. control samples as well as cases vs. control samples for the various disease groups.

The majority of the measured proteins showed a correlation to eGFR. Measured variables were correlated to eGFR using Pearson's correlations coefficient, and samples from healthy controls and all disease groups were included in the analysis. 11 and 7 proteins displayed P-values below 0.05 for plasma and urine (Table 9) respectively.

TABLE 9 Correlation analysis of eGFR and variables for all case samples URINE PLASMA Variable Pearson's r P-Value Variable Pearson's r P-Value Alpha-1- −0.08 0.3 Alpha-1- −0.33

Microglobulin Microglobulin Beta-2- −0.23 0.003 Beta-2- −0.39

Microglobulin Microglobulin Calbindin −0.16 0.04 Calbindin −0.18 <0.02 Clusterin −0.07 0.4 Clusterin −0.51

CTGF −0.08 0.3 CTGF −0.05 0.5 Creatinine −0.32

Cystatin-C −0.42 <0.0001 Cystatin-C −0.24 0.002 GST-alpha −0.12 0.1 GST-alpha −0.11 0.2 KIM-1 −0.17 0.03 KIM-1 −0.08 0.3 NGAL −0.28 <0.001 Microalbumin_UR −0.17 0.03 Osteopontin −0.33

NGAL −0.15 0.07 THP −0.31

Osteopontin −0.19 0.02 TIMP-1 −0.28 <0.001 THP −0.05 0.6 TFF3 −0.38

TIMP-1 −0.19 0.01 VEGF −0.14 0.08 TFF2 −0.09 0.3 VEGF −0.07 0.4 P values < 0.0001 are shown in bold italics P values < 0.005 are shown in bold P values < 0.05 are shown in italics

For the various disease groups, univariate statistical analysis revealed that in a direct comparison (T-test) between cases and controls, a number of proteins were differentially expressed in both urine and plasma (Table 10 and FIG. 2). In particular, clusterin showed a marked differential pattern in plasma.

TABLE 10 Differentially regulated proteins by univariate statistical analysis Group Matrix Protein p-value AA Urine Calbindin 0.016 AA Urine NGAL 0.04 AA Urine Osteopontin 0.005 AA Urine Creatinine 0.001 AA Plasma Calbindin 0.05 AA Plasma Clusterin 0.003 AA Plasma KIM-1 0.03 AA Plasma THP 0.001 AA Plasma TIMP-1 0.02 DN Urine Creatinine 0.04 DN Plasma Clusterin 0.006 DN Plasma KIM-1 0.01 GN Urine Creatinine 0.004 GN Urine Microalbumin 0.0003 GN Urine NGAL 0.05 GN Urine Osteopontin 0.05 GN Urine TFF3 0.03 GN Plasma Alpha 1 Microglobulin 0.002 GN Plasma Beta 2 Microglobulin 0.03 GN Plasma Clusterin 0.00 GN Plasma Cystatin C 0.01 GN Plasma KIM-1 0.003 GN Plasma NGAL 0.03 GN Plasma THP 0.001 GN Plasma TIMP-1 0.003 GN Plasma TFF3 0.01 GN Plasma VEGF 0.02 OU Urine Clusterin 0.02 OU Urine Microalbumin 0.007 OU Plasma Clusterin 0.00

Application of multivariate analysis yielded statistical models that predicted disease from control samples (plasma results are shown in FIG. 3)

In conclusion, these results form a valuable base for further studies on these biomarkers in urine and plasma both regarding baseline levels in normal populations and regarding the differential expression of the analytes in various disease groups. Using this panel of analytes, error rates from adaboosting and/or random forest were low enough (<10%) to allow a prediction model to differentiate between control and disease patient samples. Several of the analytes showed a greater correlation to eGFR in plasma than in urine.

Example 9: Statistical Analysis of Kidney Biomarkers in Plasma and Urine from Patients with Renal Injury

Urine and plasma samples were taken from 80 normal control group subjects and 20 subjects from each of four disorders: analgesic abuse, diabetic nephropathy, glomerulonephritis, and obstructive uropathy. The samples were analyzed for the quantity and presence of 16 different proteins (alpha-1 microglobulin (a1M), beta-2 microglobulin (β2M), calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF) as described in Example 1 above. The goal was to determine the analytes that distinguish between a normal sample and a diseased sample, a normal sample and a diabetic nephropathy (DN) sample, and finally, an diabetic nephropathy sample from the other disease samples (obstructive uropathy (DN), analgesic abuse (AA), and glomerulonephritis (GN)).

From the above protein analysis data, bootstrap analysis was used to estimate the future performance of several classification algorithms. For each bootstrap run, training data and testing data was randomly generated. Then, the following algorithms were applied on the training data to generate models and then apply the models to the testing data to make predictions: automated Matthew's classification algorithm, classification and regression tree (CART), conditional inference tree, bagging, random forest, boosting, logistic regression, SVM, and Lasso. The accuracy rate and ROC areas were recorded for each method on the prediction of the testing data. The above was repeated 100 times. The mean and the standard deviation of the accuracy rates and of the ROC areas were calculated.

The mean error rates and AUROC were calculated from urine and AUROC was calculated from plasma for 100 runs of the above method for each of the following comparisons: disease (AA+GN+OU+DN) vs. normal (FIG. 4, Table 11), DN vs. normal (FIG. 6, Table 13), DN vs. AA (FIG. 8, Table 15), OU vs. DN (FIG. 10, Table 17), and GN vs. DN (FIG. 12, Table 19).

The average relative importance of 16 different analytes (alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF) and 4 different clinical variables (weight, BMI, age, and gender) from 100 runs were analyzed with two different statistical methods—random forest (plasma and urine samples) and boosting (urine samples)—for each of the following comparisons: disease (AA+GN+OU+DN) vs. normal (FIG. 5, Table 12), DN vs. normal (FIG. 7, Table 14), DN vs. AA (FIG. 9, Table 16), OU vs. DN (FIG. 11, Table 18), and GN vs. DN (FIG. 13, Table 20).

TABLE 11 Disease v. Normal Standard Mean deviation method AUROC AUROC random 0.931 0.039 forest bagging 0.919 0.045 svm 0.915 0.032 boosting 0.911 0.06 lasso 0.897 0.044 logistic 0.891 0.041 regression ctree 0.847 0.046 cart 0.842 0.032 matt 0.83 0.023

TABLE 12 Disease v. Normal relative analyte importance Creatinine 11.606 Kidney_Injury_M 8.486 Tarnm_Horsfall_P 8.191 Total_Protein 6.928 Osteopontin 6.798 Neutrophil_Gela 6.784 Tissue_Inhibito 6.765 Vascular_Endoth 6.716 Trefoil_Factor_ 6.703 Cystatin_C 6.482 Alpha_1_Microgl 6.418 Beta_2_Microglo 6.228 Glutathione_S_T 6.053 clusterin 5.842

TABLE 13 ON v. NL Standard Mean deviation method AUROC AUROC svm 0.672 0.102 logistic 0.668 0.114 regression random 0.668 0.1 forest boosting 0.661 0.107 lasso 0.66 0.117 bagging 0.654 0.103 matt 0.642 0.087 cart 0.606 0.088 ctree 0.569 0.091

TABLE 14 DN v. NL Relative analyte importance Kidney_Injury_M 8.713 Tamm_Horsfall_P 8.448 Beta_2_Microglo 8.037 Trefoil_Factor_ 7.685 clusterin 7.394 Vascular_Endoth 7.298 Alpha_1_Microgl 6.987 Glutathione_S_T 6.959 Cystatin_C 6.920 Tissue_Inhibito 6.511 Creatinine 6.344 Neutrophil_Gela 6.305 Osteopontin 6.265 Total_Protein 6.133

TABLE 15 DN v. AA Standard Mean deviation method AUROC AUROC lasso 0.999 0.008 random 0.989 0.021 forest svm 0.988 0.039 boosting 0.988 0.022 bagging 0.972 0.036 logistic 0.969 0.057 regression cart 0.93 0.055 ctree 0.929 0.063 matt 0.862 0.12

TABLE 16 DN v. AA Relative analyte importance Creatinine 17.57 Total_Protein 10.90 Tissue_Inhibito  8.77 clusterin  6.89 Glutathione_S_T  6.24 Alpha_1_Microgl  6.15 Beta_2_Microglo  6.06 Cystatin_C  5.99 Trefoil_Factor_  5.88 Kidney_Injury_M  5.49 Vascular_Endoth  5.38 Tamm_Horsfall_P  5.33 Osteopontin  4.86 Neutrophil_Gela  4.47

TABLE 17 OU v. DN method mean_AUROC std_AUROC lasso 0.993 0.019 random 0.986 0.027 forest boosting 0.986 0.027 bagging 0.977 0.04 cart 0.962 0.045 ctree 0.954 0.05 svm 0.95 0.059 logistic 0.868 0.122 regression matt 0.862 0.111

TABLE 18 OU v. DN Relative analyte importance Creatinine 18.278 Alpha_1_Microgl  9.808 clusterin  9.002 Beta_2_Microglo  8.140 Cystatin_C  7.101 Osteopontin  6.775 Glutathione_S_T  5.731 Neutrophil_Gela  5.720 Trefoil_Factor_  5.290 Kidney_Injury_M  5.031 Total_Protein  5.030 Vascular_Endoth  4.868 Tissue_Inhibito  4.615 Tamm_Horsfall_P  4.611

TABLE 19 GN v. DN Standard deviation Mean of method AUROC AUROC lasso 0.955 0.077 random 0.912 0.076 forest bagging 0.906 0.087 boosting 0.904 0.087 svm 0.887 0.089 ctree 0.824 0.095 matt 0.793 0.114 logistic 0.788 0.134 regression cart 0.768 0.1

TABLE 20 GN v. DN Relative analyte importance Total_Protein 13.122 Creatinine 11.028 Alpha_1_Microgl  8.291 Beta_2_Microglo  7.856 Tissue_Inhibito  7.799 Glutathione_S_T  6.532 Kidney_injury_M  6.489 Osteopontin  6.424 Vascular_Endoth  6.262 Neutrophil_Gela  5.418 Trefoil_Factor_  5.382 Cystatin_C  5.339 Tamm_Horsfall_P  5.117 clusterin  4.940

Example 10: Diabetic Kidney Disease Urine Analyte Analyses

Collaborators from Texas Diabetes and Endocrinology (H1) provided urine samples for 150 patients with diabetes, of which 75 patients had kidney disease and 75 did not. The samples were analyzed using the sixteen analytes detailed in section I above. The goals of the analyses were as follows: 1) Determine if there are analytes (alone or in combination) that can separate patients with kidney disease from patients without kidney disease (controls); 2) Determine the relationships of analytes and kidney disease category to years since diagnosis, age, gender, and BMI.

Values of <LOW> were replaced by half of the minimum value for each variable. Variables with more than 50% missing values were not analyzed. Values given as ‘> nnn’ were taken as the “nnn” value following the “>” sign.

Analyte values were normalized to the urine creatinine value in the panel for each patient. Normalized value=100*the original analyte value divided by the creatinine value.

The distribution of values for most analytes was skewed, so the original values were log transformed. Analyses were performed using both the original values and the log transformed values.

In the graphs and statistical output, patients without kidney disease are labeled “NC” (normal control). Patients with kidney disease are labeled “KD” (kidney disease).

Graphs of the analyte values versus disease category (NC vs. KD) on original scale and log scale are shown in FIG. 22 and FIG. 23. Normal distribution qqplots are shown in FIG. 20 and FIG. 21. Scatterplots of each analyte versus the 24-hour microalbumin (from the clinical data) are shown FIG. 16 and FIG. 17. A graph of the kidney disease category versus years since diagnosis and of analyte values versus years since diagnosis are in FIG. 14, FIG. 15, and FIG. 24. In these graphs, red are patients with kidney disease, black are controls. It is evident that the presence of kidney disease is a function of years since diagnosis. Thus, models to predict kidney disease may perform better if the number of years since diagnosis is included as a covariate.

We performed t-tests of the values of each analyte versus disease category (NC vs. KD). Linear models of analyte versus disease category and covariates gave similar results.

TABLE 21 T-test p-values for each analyte versus disease category (NC vs. KD) using log scale. t-test Analytes p-value Microalbumin 2.68E−21 Alpha.1.Microglobulin 1.29E−05 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.004 Kidney.Injury.Molecule.1 . . . KIM.1. 0.024 Clusterin 0.037 Tamm.Horsfall.Protein . . . THP. 0.041 Connective.Tissue.Growth.Factor . . . CTGF. 0.044 Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1. 0.180 Beta.2.Microglobulin 0.334 Cystatin.C 0.348 Osteopontin 0.352 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.426 Creatinine 0.567 Calbindin 0.707 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.863 Trefoil.Factor.3 . . . TFF3. 0.878

TABLE 22 T-test p-values for each analyte versus disease category (NC vs. KD) using original scale. t-test Analytes p-value Microalbumin 1.11E−08 Alpha.1.Microglobulin 0.0007 Kidney.Injury.Molecule.1 . . . KIM.1. 0.0072 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.0190 Osteopontin 0.1191 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.1250 Beta.2.Microglobulin 0.1331 Tamm.Horsfall.Protein . . . THP. 0.1461 Cystatin.C 0.1489 Connective.Tissue.Growth.Factor . . . CTGF. 0.2746 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.3114 Calbindin 0.6189 Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1. 0.6944 Clusterin 0.7901 Trefoil.Factor.3 . . . TFF3. 0.7918 Creatinine 0.9710

We calculated the area under the ROC curve (AUROC) for classification of disease (NC vs. KD) for the following analytes and covariates: AUROC for each analyte individually (Table 23) and AUROC for individual analytes in logistic regression models that included the covariates year diagnosed, age, gender, and BMI (Table 24).

TABLE 23 AUROC for each analyte individually for classification of disease (NC vs. KD) using log scale Analytes AUROC Microalbumin 0.90 Alpha.1.Microglobulin 0.71 Kidney.Injury.Molecule.1 . . . KIM.1. 0.63 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.62 Clusterin 0.61 Tamm.Horsfall.Protein . . . THP. 0.60 Connective.Tissue.Growth.Factor . . . CTGF. 0.60 Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1. 0.58 Cystatin.C 0.56 Osteopontin 0.56 Beta.2.Microglobulin 0.56 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.55 Creatinine 0.52 Calbindin 0.51 Trefoil.Factor.3 . . . TFF3. 0.51 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.50

TABLE 24 AUROC for individual analytes in logistic regression models that included the covariates year since diagnosis, age, gender, and BMI. Analytes AUROC Microalbumin 0.90 Alpha.1.Microglobulin 0.74 Connective.Tissue.Growth.Factor . . . CTGF. 0.71 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.69 Kidney.Injury.Molecule.1 . . . KIM.1. 0.69 Tamm.Horsfall.Protein . . . THP. 0.69 Creatinine 0.69 Tissue.Inhibitor.of.Metalloproteinase.1 . . . TIMP.1. 0.68 Clusterin 0.68 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.68 Osteopontin 0.68 Calbindin 0.68 Trefoil.Factor.3 . . . TFF3. 0.68 Cystatin.C 0.67 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.67 Beta.2.Microglobulin 0.67

We calculated the area under the ROC curve (AUROC) for classification of disease (NC vs. KD) for the following combinations of analytes and covariates. For the combination of all analytes in a logistic regression model (without covariates), the AUROC=0.94. For the combination of all analytes in a logistic regression model (including covariates), the AUROC=0.95. For the combination of all analytes, excluding microalbumin, in a logistic regression model (without covariates), the AUROC=0.85. For the combination of all analytes, excluding microalbumin, in a logistic regression model (including covariates), the AUROC=0.87. Finally, we calculated the area under the ROC curve (AUROC) for classification of disease (NC vs. KD) for 24-hour clinical microalbumin from the patient record, which gave AUROC=0.97.

Example 11: Diabetic Kidney Disease Serum Analyte Analyses

This report presents the statistical analysis of the serum data for the patients detailed in Example 10 above. The samples were analyzed using fourteen of the analytes detailed in section I above. The goals of the analyses were as follows: 1) Determine if there are analytes (alone or in combination) that can separate patients with kidney disease from patients without kidney disease (controls); 2) Determine the relationships of analytes and kidney disease category to years since diagnosis, age, gender, and BMI.

Values of <LOW> were replaced by half of the minimum value for each variable. Variables with more than 50% missing values were not analyzed. The only such analyte in this data set was Calbindin. Values given as nnn′ were taken as the “nnn” value following the “>” sign.

The distribution of values for most analytes was skewed, so we log transformed the original values. We performed analyses using both the original values and the log transformed values.

In the graphs and statistical output, patients without kidney disease are labeled “NC” (normal control). Patients with kidney disease are labeled “KD” (kidney disease).

Graphs of the analyte values versus disease category (NC vs. KD) on original scale and log scale are shown in FIG. 25 and FIG. 26. Normal distribution qqplots are shown in FIG. 27 and FIG. 28. Scatterplots of each analyte versus the 24-hour microalbumin (from the clinical data) are shown in FIG. 31 and FIG. 32. Graphs of analyte values versus years since diagnosis are shown in FIG. 29 and FIG. 30. In these graphs, red are patients with kidney disease, black are controls. It is evident that analyte values and the presence of kidney disease is a function of years since diagnosis. Thus, models to predict kidney disease may perform better if the number of years since diagnosis is included as a covariate.

We performed t-tests of the values of each analyte versus disease category (NC vs. KD). Linear models of analyte versus disease category and covariates gave similar results.

TABLE 25 T-test p-values for each analyte versus disease category (NC vs. KD) using log scale. t-test Analytes p-value Alpha.1.Microglobulin . . . A1Micro. 8.03E−08 Cystatin.C 4.51E−06 Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 5.35E−06 Beta.2.Microglobulin . . . B2M. 3.88E−05 Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 4.20E−05 Kidney.Injury.Molecule.1 . . . . . KIM.1. 0.00343048 Trefoil.Factor.3 . . . TFF3. 0.05044019 Connective.Tissue.Growth.Factor . . . CTGF. 0.06501133 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.27177709 Osteopontin 0.2762483 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.33297341 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.5043943 Clusterin . . . CLU. 0.5730406

TABLE 26 T-test p-values for each analyte versus disease category (NC vs. KD) using original scale. t-test Analytes p-value Alpha.1.Microglobulin..A1Micro. 4.29E−07 Cystatin.C 5.52E−06 Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 3.19E−05 Beta.2.Microglobulin . . . B2M. 4.56E−05 Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 5.02E−05 Kidney.Injury.Molecule.1 . . . . . KIM.1. 0.000343 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.044555 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.052145 Osteopontin 0.146316 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.21544 Trefoil.Factor.3 . . . TFF3. 0.300221 Clusterin . . . CLU. 0.756401 Connective.Tissue.Growth.Factor . . . CTGF. 0.985909

We calculated the area under the ROC curve (AUROC) for classification of disease (NC vs. KD) for the following analytes and covariates using log scale. AUROC for each analyte individually (Table 27) and AUROC for individual analytes in logistic regression models that included the covariates year diagnosed, age, gender, and BMI (Table 28).

TABLE 27 AUROC for each analyte individually for classification of disease (NC vs. KD) Analytes AUROC Alpha.1.Microglobulin . . . A1Micro. 0.743154 Cystatin.C 0.705548 Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 0.695857 Beta.2.Microglobulin . . . B2M. 0.693901 Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 0.684566 Kidney.Injury.Molecule.1 . . . . . KIM.1. 0.654783 Trefoil.Factor.3 . . . TFF3. 0.617977 Connective.Tissue.Growth.Factor . . . CTGF. 0.60144 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.549698 Osteopontin 0.546497 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.541874 Clusterin . . . CLU. 0.512002 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.506312

TABLE 28 AUROC for individual analytes in logistic regression models that included the covariates year since diagnosis, age, gender, and BMI. Analytes AUROC Alpha.1.Microglobulin . . . A1Micro. 0.760846 Cystatin.C 0.731863 Tissue.Inhibitor.of.Metalloproteinases.1 . . . TIMP.1. 0.728841 Tamm.Horsfall.Urinary.Glycoprotein . . . THP. 0.725818 Beta.2.Microglobulin . . . B2M. 0.718706 Kidney.Injury.Molecule.1 . . . . . KIM.1. 0.697724 Trefoil.Factor.3 . . . TFF3. 0.689189 Connective.Tissue.Growth.Factor . . . CTGF. 0.682877 Glutathione.S.Transferase.alpha . . . GST.alpha. 0.678165 Clusterin . . . CLU. 0.676565 Vascular.Endothelial.Growth.Factor . . . VEGF. 0.674431 Osteopontin 0.673898 Neutrophil.Gelatinase.Associated.Lipocalin . . . NGAL. 0.672653

We calculated the area under the ROC curve (AUROC) for classification of disease (NC vs. KD) for the following combinations of analytes and covariates. For the combination of all analytes in a logistic regression model (without covariates), the AUROC=0.85. For the combination of all analytes in a logistic regression model (including covariates), the AUROC=0.86.

It should be appreciated by those of skill in the art that the techniques disclosed in the examples above represent techniques discovered by the inventors to function well in the practice of the invention. Those of skill in the art should, however, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments that are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention, therefore all matter set forth or shown in the accompanying drawings is to be interpreted as illustrative and not in a limiting sense. 

1.-8. (canceled)
 9. A method for generating a dataset for use in detecting diabetic nephropathy or an associated disorder in a human, the method comprising: a. performing a multiplexed immunoassay on analytes of a sample of bodily fluid selected from blood, plasma, or serum taken from a human, wherein the analytes are selected from alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, Connective tissue growth factor (CTGF), creatinine, cystatin C, Glutathione S-transferase alpha (GST-alpha), Kidney injury molecule-1 (KIM-1), microalbumin, Neutrophil gelatinase-associated lipocalin (NGAL), osteopontin, Tamm-Horsfall protein (THP), Tissue inhibitor of metalloproteinase-1 (TIMP-1), Trefoil factor 3 (TFF3), and Vascular endothelial growth factor (VEGF); b. determining the concentration for each analyte in a combination of three or more analytes in the sample to provide a sample combination dataset; c. providing a diagnostic dataset comprising a combination of three or more minimum diagnostic concentrations of the analytes indicative of a particular renal disorder; d. comparing the entries of the sample combination dataset to the entries of the diagnostic dataset; and e. generating a dataset for use in detecting diabetic nephropathy or an associated disorder in a human by selecting the diagnostic dataset entries that are less than the corresponding entries in the sample combination dataset thereby providing a matched dataset.
 10. The method of claim 9, wherein the minimum diagnostic concentration in human plasma of alpha-1 microglobulin is about 16 μg/ml, beta-2 microglobulin is about 2.2 μg/ml, calbindin is greater than about 5 ng/ml, clusterin is about 134 μg/ml, CTGF is about 16 μg/ml, cystatin C is about 1170 ng/ml, GST-alpha is about 62 ng/ml, KIM-1 is about 0.57 ng/ml, NGAL is about 375 ng/ml, osteopontin is about 25 ng/ml, THP is about 0.052 μg/ml, TIMP-1 is about 131 ng/ml, TFF-3 is about 0.49 μg/ml, and VEGF is about 855 μg/ml.
 11. The method of claim 9, wherein a combination of sample concentrations for six or more sample analytes in the test sample are determined.
 12. The method of claim 11, wherein sample concentrations are determined for the analytes selected from the group consisting of alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
 13. The method of claim 9, wherein a combination of sample concentrations for sixteen sample analytes in the test sample are determined.
 14. The method of claim 9 further comprising identifying a diabetic nephropathy or an associated disorder based on the matched dataset.
 15. A method for generating a dataset for use in detecting diabetic nephropathy or an associated disorder in a human, the method comprising: a. performing a multiplexed immunoassay on analytes of a sample of bodily fluid selected from blood, plasma, or serum taken from a human, wherein the analytes are selected from alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, Connective tissue growth factor (CTGF), creatinine, cystatin C, Glutathione S-transferase alpha (GST-alpha), Kidney injury molecule-1 (KIM-1), microalbumin, Neutrophil gelatinase-associated lipocalin (NGAL), osteopontin, Tamm-Horsfall protein (THP), Tissue inhibitor of metalloproteinase-1 (TIMP-1), Trefoil factor 3 (TFF3), and Vascular endothelial growth factor (VEGF); b. determining the concentration for each analyte in a combination of three or more analytes in the sample to provide a sample combination dataset; c. providing a diagnostic dataset comprising a combination of three or more maximum diagnostic concentrations of the analytes indicative of a particular renal disorder; d. comparing the entries of the sample combination dataset to the entries of the diagnostic dataset; and e. generating a dataset for use in detecting diabetic nephropathy or an associated disorder in a human by selecting the diagnostic dataset entries that are less than the corresponding entries in the sample combination dataset thereby providing a matched dataset.
 16. A method for generating a dataset for use in detecting diabetic nephropathy or an associated disorder in a human, the method comprising: a. performing a multiplexed immunoassay on analytes of a sample of bodily fluid selected from blood, plasma, or serum taken from a human, wherein the analytes are selected from alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, Connective tissue growth factor (CTGF), creatinine, cystatin C, Glutathione S-transferase alpha (GST-alpha), Kidney injury molecule-1 (KIM-1), microalbumin, Neutrophil gelatinase-associated lipocalin (NGAL), osteopontin, Tamm-Horsfall protein (THP), Tissue inhibitor of metalloproteinase-1 (TIMP-1), Trefoil factor 3 (TFF3), and Vascular endothelial growth factor (VEGF); b. determining the concentration for each analyte in a combination of three or more analytes in the sample to provide a sample combination dataset; c. providing a diagnostic dataset comprising a combination of three or more diagnostic concentrations for each of the analytes indicative of a particular renal disorder; d. comparing the entries of the sample combination dataset to the entries of the diagnostic dataset; and e. generating a dataset for use in detecting diabetic nephropathy or an associated disorder in a human by selecting the diagnostic dataset entries that are altered relative to the corresponding entries in the sample combination dataset thereby providing a matched dataset.
 17. The method of claim 16, wherein the combined analyte concentration may be compared to a diagnostic criterion in which the corresponding minimum or maximum diagnostic concentrations are combined using the same algebraic operations used to determine the combined analyte concentration. 